JP2009008686A - Range finder - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To prevent deterioration of ranging accuracy by reducing an operation amount by performing correlation value operation using AF data obtained at prescribed intervals among AF data within a pair of window ranges, and also by performing correlation operation using all AF data within a pair of the window ranges, using a shift amount in the window range when the maximum correlation value among the correlation values found this way is obtained as a reference and narrowing down the range within a prescribed range before and after the reference. <P>SOLUTION: Correlation value operation is performed by using AF data for every three data between a pair of AF data obtained from a pair of the window ranges. After that, the shift amount in the window range when the maximum correlation value is obtained among the correlation values obtained by a correlation value operating means is used as the reference, and within the prescribed range (re-operation range) before and after the reference, the correlation value operation (ordinary operation) is performed by using all AF data within a pair of the window ranges. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a distance measuring device, and more particularly to a distance measuring device for a camera using a passive AF sensor.

  A camera distance measuring device using a passive AF sensor images a distance measuring object using, for example, a pair of left and right line sensors, and acquires left and right sensor images (AF data). Of the AF data obtained from the pair of left and right line sensors, a pair of window ranges for acquiring a pair of AF data used for correlation value calculation is determined, and the pair of window ranges is defined as a pair of predetermined sensor regions ( A pair of AF data used for the correlation value calculation is sequentially acquired while shifting in the opposite directions in the employed sensor). Alternatively, a pair of AF data used for correlation value calculation is sequentially acquired while fixing one window range and shifting the other window range. The correlation object of the pair of AF data obtained in this way is obtained, and the distance measurement object is based on the shift amount of the window range when the maximum correlation is obtained (when the left and right sensor images in the pair of adopted sensors match). The distance is calculated.

  When obtaining the maximum correlation by calculating the correlation value of the AF data as described above, there is a problem that as the number of pixels of the AF data increases, the number of correlation calculations increases and the distance measurement time increases.

Conventionally, in order to solve this problem, calculation target pixels distributed discretely from AF data are selected, and a correlation value calculation (one-pixel correlation value calculation) is performed only on the selected calculation target pixel. A distance measurement method has been proposed in which correlation values are added for each shift amount and the shift amount is obtained based on the total correlation value (see Patent Document 1).
Japanese Patent Laid-Open No. 10-26525

  However, in the conventional distance measuring method, the distance measuring time is shortened, but there is a problem that the distance measuring accuracy is decreased. Furthermore, after the one-pixel correlation calculation of each pixel to be calculated, there is a problem that all the one-pixel correlation results need to be stored in order to add the correlation values for each shift amount.

  The present invention has been made in view of such circumstances, and an object of the present invention is to provide a distance measuring device capable of shortening the distance measuring time without reducing the distance measuring accuracy.

  In order to achieve the object, a distance measuring apparatus according to claim 1 forms an image of light from a distance measuring object on a pair of line sensors composed of a plurality of light receiving elements, and based on signals obtained from the light receiving elements. AF data generating means for generating a pair of AF data for correlation value calculation (here, the AF data is obtained by directly using sensor data obtained by integrating signals output from the respective light receiving elements of the line sensor) And AF data acquisition means for acquiring a pair of AF data from a pair of adopted sensor ranges used for ranging among the pair of line sensors, including a sensor data subjected to contrast extraction processing, etc. Within the pair of adopted sensor ranges, a pair of window ranges for acquiring a pair of AF data used for correlation value calculation is determined, and the pair of window ranges is determined as the pair of window ranges. First correlation value calculation means for sequentially calculating correlation values while shifting to obtain the highest correlation within the sensor range, and using AF data at predetermined intervals among the AF data in the pair of window ranges The first correlation value calculating means for calculating the correlation value and the shift amount of the window range when the highest correlation value among the correlation values obtained by the first correlation value calculating means is obtained as a reference. A second correlation value calculation means for sequentially calculating correlation values while shifting the pair of window ranges within a predetermined range before and after, wherein correlation value calculation is performed using all AF data in the pair of window ranges. And a second correlation value calculating means and a shift amount of the window range when the highest correlation value among the correlation values obtained by the second correlation value calculating means is obtained. It is characterized by comprising: a distance measurement object distance calculating means for calculating the release.

  That is, since the first correlation calculation means performs correlation value calculation using AF data at predetermined intervals among the AF data in the pair of window ranges, the calculation amount can be reduced, while the second The correlation calculation means includes a pair of pairs within a predetermined range before and after the shift amount of the window range when the highest correlation value among the correlation values obtained by the first correlation value calculation means is obtained. Since the correlation value is calculated using all the AF data within the window range, the ranging accuracy does not deteriorate.

  According to a second aspect of the present invention, when there are a plurality of extreme values showing a high correlation by the correlation value calculation in the employed sensor, the first correlation value calculation means determines the extreme value having the highest correlation as the provisional first pole. And the second correlation value calculating means determines that the difference between the provisional first extreme value and the provisional second extreme value is within a predetermined reference value. In this case, within the predetermined range before and after the shift amount of the window range when the provisional first extreme value is obtained, and the window range when the provisional second extreme value is obtained The correlation value calculation is performed within a predetermined range before and after the shift amount.

  This is because when the difference between the provisional first extreme value and the provisional second extreme value is within a predetermined reference value, it is unknown which extreme value is close to the highest correlation value.

  The shift amount of the window range when the highest correlation value is obtained by the first correlation value calculating means, and when the highest correlation value is obtained by the second correlation value calculating means. Means for determining a difference from the shift amount of the window range, and determining means for disabling ranging when the calculated difference is equal to or greater than a predetermined shift amount.

  When the difference between the shift amounts when the highest correlation value obtained by the first and second correlation value calculating means is obtained is not less than a predetermined shift amount, the reliability of the obtained shift amount is low, This is because there is a risk of erroneous distance measurement.

  According to a fourth aspect of the present invention, the first shift amount and the temporary second extreme value of the window range when the temporary first extreme value is obtained by the first correlation value calculating means are obtained when the temporary second extreme value is obtained. Means for determining a difference between the second shift amount of the window range and a third shift amount of the window range when the highest correlation value is obtained by the second correlation value calculating means; And determining means for disabling the distance measurement when the smaller difference is equal to or greater than a predetermined shift amount.

  That is, among the first shift amount of the first extreme value and the second shift amount of the second extreme value, the shift amount closer to the shift amount (third shift amount) of the highest correlation value; Whether or not distance measurement is possible is determined based on the magnitude of the difference from the shift amount.

  The shift amount of the window range when the highest correlation value is obtained by the first correlation value computing means and when the highest correlation value is obtained by the second correlation value computing means as shown in claim 5 The second correlation value calculating means performs an additional correlation value calculation exceeding the predetermined range by an amount of the calculated shift amount of the difference. It is characterized by doing.

  According to a sixth aspect of the present invention, the distance measuring object distance calculating means includes the highest correlation value among the correlation values obtained by the second correlation value calculating means, and the correlation values in the predetermined range before and after the correlation value. On the basis of the above, a shift amount at which a true maximum correlation value is obtained by interpolation value calculation is obtained, and a distance measurement object distance is calculated based on the shift amount. The highest correlation value obtained by the second correlation value calculating means is the highest correlation value among the discrete correlation values. According to claim 6, the shift amount of the true highest correlation value that is not discrete is calculated. Can be sought. According to the fifth aspect of the present invention, when the true maximum correlation value is obtained by the interpolation value calculation, the correlation value used for the interpolation value calculation is not deficient.

  The determination means for determining whether or not the shift amount of the window range when the highest correlation value is obtained by the first correlation value calculation means is within a short distance-side distance measurement unnecessary range. And a means for stopping the correlation value calculation by the second correlation value calculation means when the determination means determines that it is within the short distance side distance measurement unnecessary range, and the first correlation value calculation means It is characterized in that the distance of the object to be measured is calculated based on the highest correlation value obtained.

  As shown in claim 8, the pair of adopted sensors includes a sensor for the entire distance measuring area of the pair of line sensors, or each of the divided areas obtained by dividing the entire distance measuring area of the pair of line sensors into a plurality of divided areas. It is a sensor. In the latter case, even if it is impossible to perform distance measurement using a certain adopted sensor, distance measurement using another adopted sensor may be possible.

  According to the distance measuring apparatus according to the present invention, since the correlation value calculation is performed using the AF data at predetermined intervals among the AF data in the pair of window ranges, the calculation amount can be reduced. All AF data in a pair of window ranges are narrowed down to a predetermined range before and after the shift amount of the window range when the highest correlation value among the correlation values thus obtained is obtained. Since the correlation value calculation is performed using the distance measurement accuracy, the distance measurement accuracy does not decrease.

  A preferred embodiment when the distance measuring device according to the present invention is applied to, for example, a camera will be described in detail below with reference to the accompanying drawings.

  FIG. 1 is a front perspective view of a camera to which the present invention is applied. As shown in the figure, the camera 10 includes a zoom lens barrel 12 having a photographing lens for forming a subject image on a silver salt film, a strobe light emission window 16 that emits strobe light, and a photographer selects a subject. A finder window 18 for checking, an AF window 22 containing a passive AF sensor for measuring the subject distance, a photometric window 25 containing a photometric sensor for measuring the brightness of the subject, and a photographer taking a shutter. A shutter button 34 or the like that is operated when instructing the release is provided.

  FIG. 2 is a rear perspective view of the camera 10. As shown in the figure, the camera 10 is set with an LCD display panel 38 for displaying a set shooting mode, date information, etc., a flash button 42 for setting a flash emission mode, and a self-timer mode. A self-timer button 44, a focus button 46 for setting a focus mode, a date button 48 for setting a date and time, and a zoom button 50 for instructing a shooting angle of view in a wide direction or a tele direction are provided.

  For example, when the flash button 42 is operated, the mode related to the flash (strobe) can be switched. As modes that can be selected with the flash button 42, an auto mode that automatically emits strobe light when the subject is dark, There are a red-eye reduction mode that emits light and reduces red-eye, a forced light-emission mode that forcibly emits strobe light, a light-emission prohibit mode that does not emit a strobe light, and a night-scene portrait mode in which a person is photographed with a strobe. When the focus button 46 is operated, the focus mode can be switched. As a mode selectable with the focus button 46, an auto focus mode for automatically focusing, a distant view mode for photographing a distant view, and a macro shooting mode. There are modes such as macro mode.

  FIG. 3 is a block diagram showing a control unit of the camera 10. As shown in the figure, the camera 10 is provided with a CPU 60 (information processing means) for controlling the entire camera 10, acquires information from the following units, and receives the following units according to instructions from the CPU 60. Can be controlled. Note that the CPU 60 shown in FIG. 3 may be an ASIC that includes a CPU core unit and peripheral circuits such as an I / O, a watchdog timer, and an A / D converter.

  Further, as shown in the figure, the camera 10 has a regulator 62 that boosts and stabilizes the battery voltage and supplies power to the CPU 60 and other peripheral circuits, and the zoom lens barrel 12 by motor driving. A lens barrel drive unit 64 that changes the zoom position and focus position and outputs position information of the zoom position and focus position to the CPU 60, and a film feed drive unit 66 that drives the film feed motor to feed the film. Is provided.

  Further, the camera 10 includes a shutter driving unit 68 that opens and closes a shutter during exposure to expose a film, a photometric sensor 70 that measures the amount of light of a subject based on external light captured through the photometric window 25 of FIG. A strobe device 72 that charges the main capacitor and emits a strobe with the light emission energy charged in the main capacitor, and a passive that acquires data necessary for autofocusing from the subject light captured from the AF window 22 in FIG. A type AF sensor 74 is provided.

  In addition, the camera 10 is based on a programmable ROM 82 (recording means such as an EEPROM) that rewritably records various information such as parameters and data related to the control of the camera 10, processing programs, and information on distance measurement, and instructions from the CPU 60. An LCD drive unit 84 is provided for outputting a signal for displaying graphics, characters, numbers, and the like corresponding to each mode on the LCD display panel 38.

  The operation of various buttons such as the shutter button 34, the flash button 42, the self-timer button 44, the focus button 46, the date button 48, and the zoom button 50 shown in FIG. 2 is turned on / off from a switch provided corresponding to each button. It is given to the CPU 60 as an off signal. These switches are shown as switch section 86 in FIG. For the shutter button 34, a half-pressed state (SP1 is turned on) and a fully-pressed state (SP2 is turned on) are distinguished and detected.

  The driver 88 shown in FIG. 3 controls a zoom drive motor and a focus drive motor provided in the lens barrel drive unit 64 based on a command from the CPU 60, and a film provided in the film feed drive unit 66. The feeding motor can be driven. The driver 88 can output a reference voltage and driving power to the A / D conversion circuit and the photometric sensor 70 based on a command from the CPU 60. The driver 88 can output a shutter control signal that opens and closes during film exposure to the shutter drive unit 68 based on a command from the CPU 60, and can output a signal instructing flash emission / stop to the strobe device 72. It is possible.

  FIG. 4 is a diagram showing a configuration of the passive AF sensor 74. As shown in the figure, the AF sensor 74 has, for example, a lens 92 that forms an image of a subject 90 composed of two colors of white and black on the light receiving surfaces of the left and right sensors, and an image formed on the light receiving surfaces. Various data is transmitted and received between the CPU 60 and the right R (right) sensor 94 and the left L (left) sensor 96 that photoelectrically convert the obtained image and output as a luminance signal, and the R sensor 94 and the L sensor 96 A processing circuit 99 that performs control and data processing is provided. The R sensor 94, the L sensor 96, and the processing circuit 99 are mounted on the same substrate, for example.

  The R sensor 94 and the L sensor 96 are, for example, CMOS line sensors, and are composed of a plurality of cells (light receiving elements) arranged on a straight line. It is assumed that sensor numbers 1, 2, 3,... 233, 234 are assigned to the cells of the R sensor 94 and the L sensor 96 in order from the left side in the drawing. However, since the five cells on both the left and right sides of the R sensor 94 and the L sensor 96 are not actually used as dummy cells, the effective sensor areas are sensor numbers 6 to 229. From each cell of these R sensor 94 and L sensor 96, a luminance signal corresponding to the received light quantity is sequentially output to the processing circuit 99 in association with the sensor number.

  The processing circuit 99 switches between the operating state and the non-operating state of the AF sensor 74 according to an instruction signal from the CPU 60. When control data regarding the operation content is acquired from the CPU 60 in the operating state, processing such as integration processing is performed based on the control data. To start. Although details will be described later, the integration processing integrates (adds) the luminance signals of the cells obtained from the R sensors 94 and L sensors 96 for each cell, and the integrated value of the luminance signal for each cell (integrated value of the light amount). Is a process for generating If the data output from the light receiving cell of the AF sensor 74 as the value indicating the integral value of the luminance signal for each cell is referred to as sensor data, the value that the processing circuit 99 actually generates as sensor data is This is a value obtained by subtracting the integral value of the luminance signal from a predetermined reference value (reference voltage VREF). In the following description, this value is referred to as sensor data. Therefore, the sensor data shows a lower value as the amount of received light increases. However, the sensor data output from the AF sensor 74 is a value based on a signal obtained by integrating the output from each cell for each cell, and indicates the characteristics of the subject imaged by the AF sensor 74 (for example, the subject data). If it is data indicating contrast, at least the processing described below can be similarly applied. Further, in the following description, when simply referred to as integration or integration processing, integration or integration processing for obtaining sensor data (an integration value of luminance signals) is assumed.

  In addition, for example, the integration process is performed when the sensor data of any cell in a peak selection region (described later) designated by the CPU 60 in the respective sensor regions (effective cells) of the R sensor 94 and the L sensor 96 is predetermined. When the integration end value is reached, that is, when the peak value (minimum value) of the sensor data in the peak selection region reaches the integration end value, it is determined that sufficient sensor data for ranging is obtained. To do. At this time, the processing circuit 99 outputs a signal indicating the end of integration (integration end signal) to the CPU 60. As described above, the case where the peak value of the sensor data reaches the integration end value is not set as the integration end condition in the AF sensor 74. For example, the average value of the sensor data in the peak selection region reaches a predetermined value. In this case, the integration end condition may be set, and another condition may be set as the integration end condition.

  The CPU 60 receives the integration end signal and acquires the sensor data of each cell obtained by the integration process from the processing circuit 99 in association with the sensor number. As a result, the CPU 60 recognizes an image captured by the R sensor 94 and the L sensor 96 (hereinafter referred to as a sensor image). Then, as will be described in detail later, the correlation value is calculated between the respective sensor images of the R sensor 94 and the L sensor 96 (or after the contrast extraction processing of the sensor image is performed), and the correlation is highest. The amount of deviation of the sensor image is obtained, and the distance to the subject 90 is calculated (the principle of triangulation). FIGS. 5 and 6 are diagrams exemplifying sensor images (sensor data) when the distance from the AF sensor 74 to the subject 90 is short and long, respectively. When the distance to the subject 90 is short, as shown in FIG. 5, the sensor data of the sensor numbers 87 to 101 of the L sensor 96 has a bright value (50), and the sensor numbers 101 to 150 have a dark value (200). . Since the R sensor 94 is provided at a position different from that of the L sensor 96, the sensor data from sensor numbers 85 to 133 are bright values (50), and the sensor numbers 133 to 148 are dark values (200).

  On the other hand, when the distance to the subject 90 is long (for example, approximately infinity), the sensor data of the sensor numbers 87 to 117 of the L sensor 96 are bright values (50) as shown in FIG. Thus, the sensor numbers 118 to 150 are dark values (200). On the other hand, although the R sensor 94 is provided at a position different from the L sensor 96, the subject position exists at a long distance, so the sensor data from sensor numbers 85 to 116 has a bright value (50). From 117 to 148, a dark value (200) is obtained. In this case, the CPU 60 can determine that there is almost no deviation between the sensor images of the R sensor 94 and the L sensor 96 and that the subject exists at approximately infinity. On the other hand, when the subject exists at a short distance as shown in FIG. 5, the amount of deviation of the sensor image becomes large.

  Quantitatively, the subject distance takes into account the distance between the R sensor 94 and the L sensor 96, the distance from each sensor to the lens 92, the pitch of each cell of the R sensor 94 and L sensor 96 (for example, 12 μm), and the like. It can be calculated from the amount of deviation of the sensor image. The shift amount of the sensor image can be obtained by performing a correlation value calculation between the sensor images of the R sensor 94 and the L sensor 96, and details thereof will be described later.

  Next, details of the AF distance measurement process for measuring the distance of the subject using the AF sensor 74 configured as described above and focusing on the subject will be described.

  When the processing mode of the camera 10 is set to the imaging mode and the user presses the shutter button 34 halfway, the CPU 60 acquires from the switch unit 86 an SP1 ON signal indicating that the shutter button 34 has been pressed halfway. When the SP1 ON signal is acquired, the CPU 60 sets an AE corresponding to the brightness of the subject in order to capture the subject, and starts an AF distance measurement process for identifying and focusing on the subject.

  FIG. 7 is a flowchart showing an outline of the AF ranging process procedure in the CPU 60.

[Step S10 (ranging area setting process)]
The photographing lens can change the focal length by driving the zoom lens barrel 12, whereas the lens 92 that forms the sensor image on the AF sensor 74 is a fixed focus lens. Therefore, the ranging area is changed corresponding to the lens position (view angle) of the photographing lens. That is, when the photographing lens is at the tele position, the distance measuring area is narrowed.

  Here, as shown in FIG. 8, the sensor area of the R sensor 94 and the L sensor 96 is subjected to processing such as correlation value calculation for each area divided into five areas so that the subject distance is calculated for each area. It has become. If these divided areas are hereinafter referred to as divided areas, the divided areas are “right area”, “middle right area”, “center area”, “middle left area”, “left area” as shown in FIG. Is comprised. Each divided area shares a partial area (cell) with an adjacent divided area. In the correlation value calculation or the like, the correlation value calculation is individually performed between the corresponding divided areas of the R sensor 94 and the L sensor 96 (between divided areas of the same name). In the present embodiment, the divided area is obtained by dividing the sensor area into five, but may be divided into numbers other than five.

  The ranging area is an area used for ranging among the sensor areas of the R sensor 94 and the L sensor 96, and the divided area is used to determine the area. Details of the distance measurement area setting process will be described with reference to the flowchart of FIG.

  First, the CPU 60 obtains information on the currently set zoom position (angle of view angle setting) from the lens barrel drive unit 64, and the current zoom position is set to the tele side or the wide side (other than tele) other than the predetermined zoom position. (Step S10A). For example, when the zoom variable range is divided into six ranges Z1 to Z6, if the current zoom position is set to the tele end side range Z6, it is determined to be the tele side, and other ranges are set to Z1 to Z5. If it is, it is determined that it is not tele. When the macro mode is set, it is determined that it is not tele.

  If it is determined to be the tele side, as shown in FIG. 10, the distance measurement area used for distance measurement in the sensor area of R sensor 94 and L sensor 96 (range of field angle is ± 6.5 °). Is limited to a range corresponding to the angle of view of the photographic lens (range of the angle of view is ± 3.9 °). That is, when it is determined as tele, three of the “right middle area”, “central area”, and “left middle area” in the center of all the sensor areas (5 areas) of the R sensor 94 and the L sensor 96 are displayed. An area (1) composed of two divided areas is set as a distance measuring area (three area setting) (step S10B). On the other hand, if it is determined that it is not tele, an area composed of five divided areas of “right area”, “right middle area”, “center area”, “left middle area”, “left area” (2) Is set as a distance measurement area (5 area setting) (step S10C).

[Step S12 (AF data acquisition process)]
In step S12, the AF data (described later) acquisition method is switched according to the brightness of the subject.

  That is, when the luminance of the subject is very high or high, the sensor sensitivity (gain of the luminance signal) of the AF sensor 74 is set to low sensitivity, and the measurement area is set to 3 areas, the measurement is performed. When integration processing is separately performed in the “center area”, “middle left area”, and “middle right area” constituting the distance area (see area (1) in FIG. 10), and the distance measurement area is set to five areas. Are individually integrated in the “central area”, “middle left and left area”, and “middle right and right area” constituting the distance measurement area (see area (2) in FIG. 10). “Left middle and left area” indicates an area composed of “Left middle area” and “Left area”, and “Right middle and right area” includes “Right middle area” and “Right area”. Indicates the area. Further, the sensor sensitivity of the AF sensor 74 can be switched between two stages of high sensitivity and low sensitivity.

  Here, the integration processing in the “center area”, “middle left area” (or “middle left and left area”), and “middle right area” (or “middle right and right area”) constituting the distance measurement area If any sensor data in the “central area” reaches the integration end value, the sensor data for that “central area” is acquired, then the sensor data is reset and integration is started. When the sensor data of any cell in the “left middle area” (or “left middle and left area”) reaches the integration end value, the “left middle area” (or “left middle and left area”) When the sensor data is acquired, then the sensor data is reset and integration is started, and when the sensor data of any cell in the “Right Middle Area” (or “Right Middle and Right Area”) reaches the integration end value , Its “right middle area” (or “right middle Say that acquires sensor data of the right area "). In this way, by performing integration processing of multiple areas individually, even if high-intensity light or the like enters one of the areas, and the sensor data in that area is inappropriate, the sensor is effective from other areas. Data can be acquired. For example, when the distance measurement area is set to five areas, a person who is a main subject as shown in FIGS. 11A and 11B and a high-intensity light behind the person exist in the distance measurement area. To do. At this time, for example, if integration processing is performed with the entire area of the ranging area as a selection area (peak selection area), the signal level of the sensor data in the right area corresponding to the high-intensity light as shown in FIG. Becomes appropriate, and the signal level of the sensor data in the central area corresponding to the person who is the main subject is reduced. For this reason, when trying to obtain the subject distance for each divided area, it is determined that distance measurement is impossible for the central area, and as a result, there is a problem that the rear light is focused. On the other hand, when the ranging area is divided into a plurality of areas and individually integrated as described above, it corresponds to the person who is the main subject in the integration processing of the central area as shown in FIG. Thus, the signal level of the sensor data becomes appropriate, and as a result, the person can be focused.

  Further, when the luminance of the subject is medium, the sensor sensitivity of the AF sensor 74 is set to low sensitivity, and integration processing is collectively performed in the ranging area set to 3 areas or 5 areas. For example, in the case of setting three areas, integration processing of “center area”, “middle left area”, and “middle right area” constituting the ranging area (see area (1) in FIG. 10) is performed simultaneously. When the sensor data of any cell in these “central area”, “middle left area”, and “middle right area” reaches the integration end value, “middle area”, “middle left area”, and “right Collect the sensor data of “middle area” at once.

  Furthermore, when the luminance of the subject is low, the sensor sensitivity of the AF sensor 74 is set to high sensitivity, and integration processing is performed in a range-finding area that is set to 3 areas or 5 areas. If the sensor data of the cells in the distance measuring area does not reach the integration end value even after the integration time has elapsed, the integration is terminated and the sensor sensitivity of the AF sensor 74 is switched to low sensitivity. Then, integration is started and auxiliary light for autofocus is emitted from the flash device 72 (AF pre-emission). In this case, integration processing in the ranging area set to 3 areas or 5 areas is performed collectively.

  Here, the data output from the light receiving cell of the AF sensor 74 is used as sensor data, and the image data used in the following contrast detection process 1 or later is the sensor data in addition to the sensor data itself. Contrast extraction processing and the like may be performed. Therefore, the processing after the contrast detection processing 1 is a generic term for the sensor data used for the processing as it is, and the sensor data subjected to the contrast extraction processing or the like. And described as AF data.

[Step S14 (Contrast Detection Process 1)]
In step S14, it is determined whether or not the AF data acquired in step S12 has a contrast necessary for distance measurement. If it is determined that the AF data does not have the contrast necessary for distance measurement (low contrast determination), distance measurement is disabled.

  Here, in the ranging area setting process in step S10, when three areas are set as the ranging area, the above contrast determination is performed for each of the divided areas of the middle right area, the central area, and the middle left area. The processing such as the correlation value calculation using the AF data of the divided area determined as low contrast is not performed. Similarly, when five areas are set as the distance measurement area, the above contrast determination is performed for each divided area of the right area, the middle right area, the center area, the middle left area, and the left area, and the low contrast determination is performed. Processing such as correlation value calculation using AF data of the divided areas is not performed.

[Step S16 (Correlation Value Calculation Processing)]
In step S16, a correlation value calculation is performed between the sensor images (AF data) acquired from the R sensor 94 and the L sensor 96 of the AF sensor 74, and the sensor image shift amount (left and right AFs) when the correlation is the highest. The shift amount between data) is obtained. The distance of the subject can be obtained from the shift amount between the left and right AF data.

  When three areas are set as the distance measurement area, the correlation value is calculated for each divided area of the middle right area, the center area, and the middle left area, and five areas are set as the distance measurement area. If there is, the correlation value calculation is performed for each divided area of the right area, the middle right area, the center area, the middle left area, and the left area, but the determination of low contrast (distance cannot be measured) is performed in step S14. No correlation value calculation is performed in the divided areas.

  Next, the correlation value calculation will be described with reference to FIG.

  In FIG. 12, 94A and 96A are sensors in certain divided areas (hereinafter referred to as “adopted sensors”) of the R sensor 94 and the L sensor 96, respectively. Reference numerals 94B and 96B denote an R window and an L window for extracting AF data used for correlation value calculation from AF data of the employed sensors 94A and 96A, respectively.

  Here, if the shift amount between the R window 94B and the L window 96B is n (n = −2, −1, 0, 1,..., MAX (= 38)), the R window 94B when n = −2. Is located at the left end of the adoption sensor 94A, and the L window 96B is located at the right end of the adoption sensor 96A. When n = −1, the L window 96B is shifted to the left by one cell from the right end of the employed sensor 96A, and when n = 0, the R window 94B is shifted to the right by one cell from the left end of the employed sensor 94A. Similarly, every time n increases by 1, the R window 94B and the L window 96B alternately move one cell at a time. When n = MAX, the R window 94B is located at the right end of the adopted sensor 94A, and the L window 96B is located at the left end of the adopted sensor 96A.

  Assuming that the correlation value f (n) between the R window 94B and the L window 96B at a certain shift amount n is f (n), the correlation value f (n) is given by

Can be expressed as In equation (1), i is a number indicating the position of a cell in the window (i = 1, 2,... Wo (= 42)), and R (i) and L (i) are R windows. 94B and AF data obtained from the cell at the same cell position i in the L window 96B. That is, as shown in the equation (1), the correlation value f (n) is a sum of absolute values of differences between AF data obtained from cells at the same cell position in the R window 94B and the L window 96B, and has a high correlation. The closer to zero.

  Accordingly, the correlation value f (n) can be obtained by changing the shift amount n, and the subject distance can be obtained from the shift amount n when the correlation value f (n) is the smallest (when the correlation is the highest). It should be noted that when the subject distance is infinity, the correlation is highest when the shift amount is n = 0, and when the subject distance is the closest end, the subject image is R so that the correlation is highest when the shift amount is n = MAX. An image is formed on the sensor 94 and the L sensor 96. Further, the arithmetic expression for obtaining the correlation is not limited to the above expression (1), and other arithmetic expressions can be used. In such a case, the correlation value may increase as the correlation increases. In this case, the magnitude relationship of the correlation value in the following description is inverted, and the present embodiment is applied to the arithmetic expression. For example, the minimum value of the correlation value calculated by the above equation (1) becomes the maximum value, and the wording of the correlation value calculated by the above equation (1) is reversed to the wording of the large or small value. And can be applied.

[Step S18 (contrast detection process 2)]
In step S14, it is determined whether or not the AF data in the divided area has a contrast necessary for distance measurement. In step S18, the AF data in the window range at the time of the shift amount n that maximizes the correlation is determined. It is determined whether the AF data has a contrast necessary for distance measurement. If it is determined that the contrast is low, distance measurement is disabled, and distance measurement based on the shift amount n at that time is not performed.

[Step S20 (L and R channel difference correction processing)]
In step S20, the left and right AF data obtained from the AF sensor 74 and the minimum values of the left and right AF data within the window range where the correlation is maximum are compared. Then, when the absolute value of the difference between the minimum values of the left and right AF data is not less than the first reference value and not more than the second reference value, the AF data of the channel that does not exceed the dynamic range is corrected. If the correlation value when the correlation is maximum is small, it can be determined that the correlation value calculation result is highly reliable without correcting the AF data. The AF data may be corrected only when the value is greater than or equal to the third reference value.

  When the AF data is corrected, the correlation value is calculated again to obtain the minimum correlation value. Then, the minimum correlation value after correction is compared with the minimum correlation value before correction, and the shift amount of the correlation value having the higher degree of coincidence is employed.

[Step S22 (Interpolation Value Calculation Processing)]
In step S22, after obtaining a correlation value f (n) (minimum minimum value) when the correlation is highest, the minimum minimum value and the preceding and following correlation values are used, and the shift amount is within 1 (AF sensor 74). The interpolated value is calculated within 1 pitch of the cell.

  The interpolation value is based on the minimum minimum value and correlation values (a minimum of three correlation values) at a plurality of shift amounts before and after the shift amount n, where n is the shift amount at which the minimum minimum value is obtained. Then, an intersection of two straight lines that intersect in a V shape passing through these correlation values is obtained, and calculated as a difference value between the position of the intersection and the shift amount n.

[Step S24 (AF error processing)]
In step S24, if it is determined that distance measurement is not possible in all the distance measurement areas of the three area settings or the five area settings, the photographic lens is set so as to focus on a preset subject distance.

  In other words, when the auxiliary light for autofocus is emitted and it is determined that an error occurs due to insufficient AF data amount in all the distance measurement areas, the photographing lens is set so as to focus at infinity.

  Further, when an auxiliary light for autofocus is emitted and an error is determined due to insufficient AF data amount in all the distance measurement areas, the fixed focus set distance that can be reached by the strobe is switched according to the film sensitivity. For example, in the case of ISO 400 or more, the fixed focus set distance is 6 m, and in the case of less than ISO 400, the fixed focus set distance is 3 m. Furthermore, the fixed focus set distance to be focused may be switched depending on the type of error.

[Step S26 (distance calculation processing)]
In step S26, the subject distance is calculated based on the shift amount n when the minimum correlation value is obtained by the correlation value calculation in step S16 and the interpolation value calculated in step S22. It should be noted that the subject distance is calculated for every distance measuring area of the three areas or the five areas set.

[Step S28 (area selection process)]
If no error occurs during the AF distance measurement process, three subject distances are calculated when the three areas are set, and five subject distances are calculated when the five areas are set. When a plurality of subject distances are calculated, the closest subject distance is basically adopted.

  When five areas are set, five subject distances are calculated, and among these subject distances, the subject distance corresponding to one of the left area and the right area is the very close distance, and the other areas When all the corresponding subject distances are beyond the intermediate distance, the result of the closest distance is not adopted, and the subject distance closest to the subject distances beyond the intermediate distance is adopted.

{Details of AF data acquisition process (step S12 in FIG. 7)}
Next, the AF data acquisition process in step S12 of FIG. 7 will be described in detail.

  First, the peak selection area will be described. The peak selection area refers to a range of cells for monitoring whether or not the peak value (minimum value) of sensor data has reached the integration end value in the integration process of the AF sensor 74. . FIG. 13 is a diagram showing a mode of the peak selection region set in the AF data acquisition process described below. The peak selection area is configured with the divided areas described in FIG. 8 as a unit, and FIG. 13A shows the divided areas of the R sensor 94 and the L sensor 96. On the other hand, the peak selection region is shown in FIGS. (B) to (H), and the peak selection region is switched to seven regions (1) to (7).

  The area (1) shown in FIG. 5B is composed of three divided areas of “center area”, “right middle area”, and “left middle area” in the R sensor 94 and the L sensor 96. The area (2) shown in C) includes five divided areas of the “center area”, “right middle area”, “right area”, “left middle area”, and “left area” in the R sensor 94 and the L sensor 96. Consists of The area (1) is equal to the area (1) when the three areas are set as shown in FIG. 10, and the area (2) is equal to the area (2) when the five areas are set as shown in FIG. Regions (3), (4), and (5) shown in FIGS. (D), (E), and (F) are “center area”, “left middle area”, “ The area (6) shown in FIG. 6G is composed of the left center area and the left area in the R sensor 94 and the L sensor 96, and the area (7) shown in FIG. , The R sensor 94 and the L sensor 96 are composed of a middle right area and a right area.

  FIG. 14 is a flowchart showing the procedure of AF data acquisition processing. First, the CPU 60 refers to the signal output of the light amount output from the photometric sensor 70 and determines whether or not the light amount obtained from the subject is greater than or equal to a predetermined threshold value for determining ultra-high brightness (step S50). When it is determined NO, the CPU 60 causes the AF sensor 74 (the processing circuit 99 of the AF sensor 74) to start batch gain high integration processing (step S52). In the following description, even if the processing is performed by the processing circuit 99 of the AF sensor 74, the processing circuit 99 is not specified and is simply described as the AF sensor 74.

  In the collective gain high integration, an area in the same range as the distance measurement area set by the distance measurement area setting process in step S10 shown in FIG. 7 is set as a peak selection area, and the sensor sensitivity of the AF sensor 74 is set as high sensitivity. This is a process of acquiring sensor data of each cell in the distance measurement area at once. When the distance measurement area is set to 3 areas in the distance measurement area setting process (when the zoom position is tele), the peak selection area is set to the area (1) shown in FIG. On the other hand, when the distance measurement area is set to 5 areas (when the zoom position is other than tele), the peak selection area is set to the peak selection area (2) shown in FIG. The collective gain high integration is an integration process for acquiring sensor data when the subject brightness is low. However, as can be seen from the description below, the subject gain is high brightness, medium brightness, or low brightness. It is also a process for determining whether or not it is in a state. Instead of the collective gain high integration process in step S52, another method for determining the subject brightness may be employed.

  After the batch gain high integration by the AF sensor 74 is started, the CPU 60 waits for an integration end signal indicating the end of the integration process to be output from the AF sensor 74. Then, after the integration process is started, the time (integration time) required for the integration process to end is less than 2 ms, 2 ms to less than 4 ms, or 4 ms or more (whether the integration process does not end even after 4 ms elapses) ) Is determined (step S54).

  If the integration time is less than 2 ms, it is determined that the subject has high brightness, the sensor data of the AF sensor 74 is reset, and the process is switched to a three-division gain low integration process described later in detail (step S56). ). If the integration time is not less than 2 ms and less than 4 ms, it is determined that the subject has medium luminance, the sensor data of the AF sensor 74 is reset, and the processing is switched to a collective gain low integration process described later in detail (step S58). . If the integration time is 4 ms or more, it is determined that the subject has low luminance, and low luminance processing described in detail later is executed (step S60).

  If YES in step S50, that is, if it is determined that the subject has an extremely high brightness, the process of three-fold gain low integration is executed in the same manner as when the process is completed in less than 2 ms (step S56).

  The three-divided gain low integration process in step S56 sets the sensor sensitivity of the AF sensor 74 to a low sensitivity, and also divides the distance measurement area set in the distance measurement area setting process into three, and each divided area is divided. This is processing of the CPU 60 that causes the AF sensor 74 to execute integration processing in order as a peak selection region.

  That is, when the distance measurement area is set to 3 areas in the distance measurement area setting process of step S10 in FIG. 7 (when the zoom position is tele), the distance measurement areas are “center area”, “middle left area” and “middle right” It consists of “area”. This distance measuring area is divided into three divided areas of “central area”, “middle left area”, and “middle right area”, and integration processing is executed with each divided area as a peak selection area in order. In the mode of the peak selection region shown in FIG. 13, the regions (3), (4), and (5) in FIGS. (D), (E), and (F) are set as the peak selection region in order. Specifically, as shown in the flowchart of FIG. 15, first, the central area (region (3)) is set as the peak selection region (step S80), and the sensor sensitivity of the AF sensor 74 is set low (step S82). Integration processing is started (step S84). When the integration process is completed and the CPU 60 acquires the sensor data of the area (3) (step S86), since the distance measurement area is set to 3 areas (step S88), the left middle area (area (4) ) As a peak selection region (step S90), and the sensor sensitivity of the AF sensor 74 is set low (step S92), and integration processing is started (step S94). When this integration process is completed and the CPU 60 acquires the sensor data of the region (4) (step S96), the right middle area (region (5)) is set as the peak selection region (step S98), and the AF sensor The sensor sensitivity of 74 is set to low sensitivity (step S100), and integration processing is started (step S102). When this integration process ends and the CPU 60 acquires the sensor data of the region (5) (step S104), the three-divided gain low integration process ends. As a result, sensor data of each divided area in the distance measurement area necessary for the subsequent distance calculation is acquired.

  On the other hand, when 5 areas are set in the distance measurement area setting process (when the zoom position is other than tele), the distance measurement areas are “center area”, “middle left area”, “left area”, “middle right area”. ”And“ Right area ”. This distance measuring area is divided into three regions (3), (6), and (7) in FIGS. 13D, 13G, and 13H, and each region (3), (6), (7) Are sequentially executed using the peak selection region. Specifically, with reference to the flowchart of FIG. 15, as in the case of the three area setting described above, first, the central area (region (3)) is set as the peak selection region (step S80). The sensor sensitivity is set to low sensitivity (step S82) and integration processing is started (step S84). When this integration process ends and the CPU 60 acquires the sensor data of the area (3) (step S86), since the distance measurement area is set to 5 areas (step S88), the left middle area and the left area (area) (6)) is set as the peak selection region (step S106), and the sensitivity of the AF sensor 74 is set low (step S108), and the integration process is started (step S110). When this integration process is completed and the CPU 60 acquires the sensor data of the area (6) (step S112), the right middle area and the right area (area (7)) are set as the peak selection areas (step S114). Then, the sensor sensitivity of the AF sensor 74 is set to low sensitivity (step S116), and integration processing is started (step S118). When this integration process ends and the CPU 60 acquires the sensor data of the region (7) (step S120), the three-divided gain low integration process ends. As a result, sensor data of each divided area in the distance measurement area necessary for the subsequent distance calculation is acquired.

  In the above description, when the distance measurement area is set to five areas, the distance measurement area is divided into three areas (3), (6), and (7). The sensor data may be acquired by performing integration processing with each divided area in turn as a peak selection region. That is, the number of areas into which the ranging area is divided is not limited to the case of the present embodiment, and can be arbitrarily set and changed.

  The collective gain low integration process in step S58 of FIG. 14 does not change the peak selection region adopted in the collective gain high integration of step S52, and switches the sensor sensitivity of the AF sensor 74 from high sensitivity to low sensitivity. This is a process of the CPU 60 that causes the integration process to be executed.

  That is, when the distance measurement area is set to 3 areas in the distance measurement area setting process (when the zoom position is tele), the peak selection area is the area (1) shown in FIG. The AF sensor 74 is made to perform integration processing with the sensor sensitivity of the AF sensor 74 being low. On the other hand, when the distance measurement area is set to five areas in the distance measurement area setting process (when the zoom position is other than tele), the peak selection area is the area (2) shown in FIG. In addition, the AF sensor 74 is made to perform integration processing with the sensor sensitivity of the AF sensor 74 being low.

  When the above integration processing is completed, the CPU 60 acquires sensor data in the distance measurement area from the AF sensor 74. Thereby, sensor data of each divided area in the distance measurement area necessary for distance measurement calculation is acquired.

  The low-brightness process in step S60 of FIG. 14 is a process in which the collective gain high integration process in step S52 is continued until the integration process ends with the maximum allowable integration time as a limit.

  The maximum allowable integration time differs depending on the shooting mode. When the shooting mode is set to the light emission inhibition mode, the maximum allowable integration time is 200 ms even after the integration time of the collective gain high integration in step S52 is 4 ms or more. ) Is continued as it is. If the integration process ends normally within 200 ms (if the integration ends when the peak value of the sensor data in the peak selection region reaches the integration end value (the same applies hereinafter)), the sensor at that time Data is acquired from the AF sensor 74. On the other hand, if the integration process does not end normally even after 200 ms elapses, the integration process is forcibly ended by an instruction signal from the CPU 60, and sensor data at that time is acquired from the AF sensor 74.

  If the photographing mode is not the light emission prohibition mode, the integration process is continued as it is with a limit of 100 ms (maximum allowable integration time) after the integration time of the collective gain high integration in step S52 is 4 ms or longer. If the integration process ends normally within 100 ms, sensor data is acquired from the AF sensor 74 at that time. On the other hand, if the integration process does not end even when the integration time reaches 100 ms, the integration process is forcibly ended by an instruction signal from the CPU 60. Then, the integration process is restarted while the AF pre-light is emitted by the auxiliary light emitted by the strobe device AF72. Note that when the night scene and the person in the foreground are photographed at the same time as in the night scene portrait mode, the integration time for the collective gain high integration is used to prevent the problem of focusing on the night scene. Is limited to 25 ms instead of 100 ms, and if the integration process does not end normally even if the integration time reaches 25 ms, the integration process is forcibly ended and the integration process is started together with AF pre-emission. Hereinafter, processing for performing integration while causing AF pre-emission is referred to as pre-emission processing.

  When integration processing by pre-emission processing is started, the peak selection region employed in the collective gain high integration in step S52 is not changed, and the sensor sensitivity of the AF sensor 74 is switched from high sensitivity to low sensitivity and integration processing is performed on the AF sensor 74. To start. The AF pre-emission is performed by setting a predetermined upper limit number of times by intermittent pulse emission. As a result, when the integration process is normally completed before the pre-emission reaches the upper limit number, the sensor data at that time is acquired from the AF sensor 74. On the other hand, if the integration process does not end normally even if the pre-emission reaches the upper limit, the integration process is forcibly ended and sensor data at that time is acquired from the AF sensor 74.

  In the above embodiment, the distance measurement area is divided into a plurality of areas (peak selection areas) only when the subject brightness is high or very high, and sensor data is acquired for each region. Even when the luminance is medium luminance or low luminance, the distance measurement area may be divided into a plurality of areas as in the case of high luminance or the like, and sensor data may be acquired for each area.

  Further, in the above embodiment, the process of acquiring sensor data corresponding to each case is performed by dividing into cases where the subject brightness is ultra-high brightness, high brightness, medium brightness, and low brightness. However, the subject luminance level may be finer or coarser than that in the above embodiment, and sensor data acquisition processing corresponding to each subject luminance level may be performed.

  Next, processing operations of the CPU 60 and the AF sensor 74 (processing circuit 99) when executing the AF data acquisition processing will be described in detail. As shown in FIG. 16, various signals are transmitted and received between the CPU 60 and the AF sensor 74 through a plurality of signal lines. As a signal line of a signal transmitted from the CPU 60 to the AF sensor 74, a signal for switching the AF sensor 74 to an operation state or a non-operation state is transmitted / AFCEN, and a signal instructing setting of control data is transmitted / AFRST AFAD to which a signal indicating the contents of control data is transmitted, and AFCLK to which a READ / WRIGHT-clock pulse is transmitted. As a signal line of a signal transmitted from the AF sensor 74 to the CPU 60, a signal indicating that the integration process is completed is transmitted / AFEND, and a peak value (minimum value) of sensor data in the peak selection region is transmitted as analog data. MDATA, the R sensor 94, and the AF sensor that transmits the sensor data of each cell of the L sensor 96 as analog data. Hereinafter, the type of signal transmitted from each signal line is identified by the name of the signal line (/ AFCEN signal, / AFRST signal, etc.).

  The operation timing of transmission / reception of each signal in the CPU 60 and the AF sensor 74 will be described with reference to the operation timing chart of FIG. When the CPU 60 sets the / AFCEN signal to 1 (High level), the AF sensor 74 is in a non-operating state, and when the CPU 60 switches the / AFCEN signal to 0 (Low level), the AF sensor 74 switches to an operating state. (See time T10).

  When a predetermined time (10 ms) elapses after the AF sensor 74 is switched to the operating state, the CPU 60 switches the / AFRST signal from 1 to 0 and instructs the AF sensor 74 to set control data (see time T20). . Then, the CPU 60 transmits control data by the AFAD signal to the AF sensor 74 and also transmits a clock pulse by the AFCLK signal (refer to the period from time T20 to T30). When the / AFRST signal is switched from 1 to 0, the AF sensor 74 reads the signal level of the AFAD signal in synchronization with the clock pulse given by the AFCLK signal. As a result, data necessary for integration processing such as a peak selection region and sensor sensitivity is set in the AF sensor 74. As control data, 128 pieces of data indicated by 1 or 0 from D0 to D127 are transmitted in time series. The contents of the control data will be described later.

  When the last data (D127) is transmitted by the AFAD signal (see time T30) and 100 μs has elapsed (see time T40), the CPU 60 switches the / AFRST signal from 0 to 1, and instructs the start of integration processing. Thereby, the AF sensor 74 starts light reception by each cell of the R sensor 94 and the L sensor 96 after 150 μs when the sensor sensitivity is set to high sensitivity and after 30 μs when the sensor sensitivity is set to low sensitivity. At the same time, integration of the luminance signal sequentially output from each cell is started (see time T50). At the same time, the AF sensor 74 switches the / AFEND signal from 1 to 0, and transmits to the CPU 60 that integration has started. Further, the peak value of the sensor data in the peak selection region is output as analog data by the MDATA signal.

  When the peak value of the sensor data reaches a predetermined integration end value VEND (for example, 0.5 V) after the start of the integration process, the AF sensor 74 ends the integration of the luminance signal and changes the / AFEND signal from 0 to 1. (Refer to time T60). Note that switching of the / AFEND signal from 0 to 1 is an integration end signal.

  The CPU 60 detects the integration time by detecting the time when the / AFEND signal is set to 0 (period from time T50 to T60), and the integration is performed when the / AFEND signal is switched from 0 to 1. Detect that it has finished.

  When the integration is completed, the CPU 60 transmits a clock pulse by the AFCLK signal to the AF sensor 74 and instructs reading of the sensor data (see time T70). In addition, when the peak value of the sensor data in the peak selection area reaches the integration end value, the auto end mode in which the AF sensor 74 automatically ends the integration, and whether the peak value of the sensor data has reached the integration end value. Regardless of whether or not, there is an external termination mode in which integration is terminated by an instruction from the outside (CPU 60). In the former case, the CPU 60 keeps the AFAD signal set to 1 and transmits the clock pulse. In the latter case, the AFAD signal is switched to 0 to complete the integration of the AF sensor 74, and then the clock pulse is transmitted. Even in the former case, the CPU 60 can forcibly terminate the integration in the AF sensor 74 by switching the AFAD signal from 1 to 0.

  The AF sensor 74 alternately converts the sensor data obtained by integrating each cell in synchronization with the clock pulse given by the AFCLK signal from the sensor numbers 1 to 234 of the L sensor 96 and the R sensor 94 as analog data. Send to. As a result, the CPU 60 acquires sensor data from the AF sensor 74.

  Next, the contents of the control data transmitted / received by the AFAD signal will be described. As described above, the control data is composed of 128 pieces of 1 or 0 data from D0 to D127 (see the period of time T20 to T30 in FIG. 17). Among these, D0 to D111 are peak selection region setting data indicating the peak selection region to be set in the AF sensor 74, and D112 to D118 are peak selection region number data indicating the number of peak selection regions. Details of the peak selection area setting data and the peak selection area number data will be described later.

  D119 to D120 are dummy data (0), and D121 is sensitivity data indicating sensor sensitivity to be set. In the present embodiment, the sensor sensitivity can be switched in two steps, high or low. When D121 is 1, high sensitivity is indicated, and when D121 is 0, low sensitivity is indicated.

  D122 is integration mode data indicating a mode related to the end of integration. When D122 is 1, it indicates the setting of an external end mode in which integration is ended by an instruction from the outside. When D122 is 0, it is within the peak selection region. This indicates the setting of the automatic end mode in which the AF sensor 74 automatically ends the integration process when the sensor data reaches a predetermined integration end value (integration end voltage) VEND.

  D123 is automatic integration end voltage setting data for setting the integration end value VEND in the case of the automatic end mode. In this embodiment, when D123 is 1, it indicates the setting of the voltage L, and when D123 is 0 Shows the setting of the voltage H.

  D124 to D126 are VREF selection data for setting the reference voltage VREF. Eight types of reference voltages can be set by 3-bit data. D127 is end data indicating the end of the control data, and is always set to 1.

  Next, the peak selection region setting data D0 to D111 and the peak selection region number data D112 to D118 will be described in detail. Each of the R sensor 94 and the L sensor 96 includes 234 cells having sensor numbers 1 to 234 as shown in FIG. The left and right five cells (sensor numbers 1 to 5 and sensor numbers 230 to 234) of the sensors 94 and 96 are dummy cells, and the actually effective cells (effective pixels) are 224 from sensor numbers 6 to 229. It has become.

  In the processing of the CPU 60 and the AF sensor 74, the cells having sensor numbers 6 to 229 in the effective pixel range are managed in units of blocks with four adjacent cells as one block. As shown in FIG. Block numbers D0, D1,..., D55 (number of blocks 56) are assigned in order from the sensor number 229 to sensor number 6 of the R sensor 94, and the blocks from the sensor number 229 to the sensor number 6 of the R sensor 94 are sequentially blocked in units of four. Numbers D56, D57,..., D111 (number of blocks 56) are assigned.

  The data of D0 to D111 transmitted / received as control data between the CPU 60 and the AF sensor 74 corresponds to the block numbers assigned in this way, and the peak selection region setting data D0 to D111 are shown in FIG. , The setting data for the L sensor 96 is D0 to D55 not surrounded by a dotted line, and the setting data for the R sensor 94 is D56 to D111 surrounded by a dotted line. For example, the setting data D0 and D56 indicate the setting data for the sensor numbers 226 to 229 of the L sensor 96 and the R sensor 94, respectively, and the setting data D55 and D111 respectively indicate the sensor numbers 6 to 6 of the L sensor 96 and the R sensor 94. The setting data for 9 is shown.

  The peak selection area setting data D0 to D111 indicate whether or not four cells having a block number corresponding to the setting data are set as cells in the peak selection area. The four cells with the block number corresponding to the data are set as cells in the peak selection area. When the setting data is 0, the four cells with the block number corresponding to the setting data are set as cells outside the peak selection area. Is done. For example, when the setting data D0 is 1, the cells 229, 228, 227, and 226 of the L sensor 96 are set as cells in the peak selection region.

  In addition, the peak selection area number data D112 to D118 transmitted and received as control data together with the peak selection area setting data indicate the number of blocks set as the peak selection area by the peak selection area setting data in binary number. As shown, the number of blocks to be set as the peak selection region is represented by 7-bit data in which D112 is the most significant bit and D118 is the least significant bit. When only D115 is 1 as shown in FIG. 11A, the number of blocks set as the peak selection region is 8, and D112 to D114 are 1 and D115 to D118 are 0 as shown in FIG. In this case, 112 blocks are set as the peak selection region.

  Next, a procedure for generating peak selection region setting data will be described. The peak selection area is set to any one of the areas (1) to (7) as shown in FIG. The peak selection area setting data when setting each area (1) to (7) as the peak selection area is generated as follows.

  For example, as shown in FIG. 21, when the region P (shaded portion) is set as the peak selection region in the sensor region S of the R sensor 94 or the L sensor 96, the sensor numbers of the rightmost and leftmost cells of the region P are obtained and the sensor A cell whose number is between the sensor numbers of the rightmost and leftmost cells in the region P is defined as a cell in the peak selection region.

  Here, the sensor number of each cell indicates the address of each cell, and in particular, the right end address of the region P is the peak selection start address PS and the left end address is the peak selection end address PE.

On the other hand, as information specifying the region P, the rightmost address S1 of the region P, the address S2 of a predetermined cell in the region P, and the number of cells (number of sensors) D from the cell of the address S2 to the leftmost cell of the region P are It is assumed that it is given in advance as reference data. At this time, the peak selection region start address PS and the peak selection region end address PE of the region P are expressed by the following equations:
[Equation 2]
PS = S1 (2)
PE = S2 + D-1 (3)
It can ask for. In the R sensor 94, the peak selection start address PS, peak selection end address PE, and reference data S1, S2, D for the region to be the peak selection region are PSR, PER, SR1, SR2, DR, respectively, and the L sensor 96 The peak selection start address PS, peak selection end address PE, and reference data S1, S2, and D for the region to be selected as the peak selection region are identified as PSL, PEL, SL1, SL2, and DL, respectively.

  The case where each region (1) to (7) shown in FIG. 13 is set as the peak selection region will be described in detail. As reference data when each region (1) to (7) is set as the peak selection region, The address of the rightmost cell in each divided area of the R sensor 94 and the L sensor 96 and the number of employed sensors (number of cells) in each divided area are used.

  FIG. 22 shows a specific numerical example of the address of the cell at the right end of each divided area and the number of adopted sensors in each divided area adopted for the R sensor 94 and the L sensor 96 in the present embodiment. The R sensor 94 is indicated by numerical values without parentheses, and the L sensor 96 is indicated by numerical values with parentheses.

For example, when the region (1) is the peak selection region, the reference data SR1, SR2, and DR for the R sensor 94 are respectively the rightmost cell address 46 in the left middle area and the rightmost cell address 126 in the right middle area. The number of employed sensors in the right middle area is 62. Similarly, the reference data SL1, SL2, and DL for the L sensor 96 are the address 48 of the rightmost cell in the middle left area, the address 128 of the rightmost cell in the middle right area, and the number of employed sensors 62 in the middle right area. By substituting these reference data into the above equations (2) and (3), peak selection start addresses PSR and PSL, peak selection end addresses PER and PEL of the region (1) in the R sensor 94 and the L sensor 96 are calculated. . That is,
[Equation 3]
PSR = 46
PER = 126 + 62-1 = 187
PSL = 48
PEL = 128 + 62-1 = 189
Is calculated. Accordingly, when the region (1) is set as the peak selection region, it is determined that the cells with sensor numbers 46 to 187 are the cells in the peak selection region for the R sensor 94, and the sensor numbers 48 to 189 are for the L sensor 96. Are determined to be cells in the peak selection region.

  When the regions (2) to (7) other than the region (1) are used as the peak selection regions, the peak selection start addresses PSR and PSL and the peak selection end addresses PER and PEL can be calculated in the same manner as described above. That is, in the peak selection region to be set, the right end address in the rightmost divided area is set as reference data SR1 and SL1, and the right end address in the leftmost divided area is set as reference data SR2 and SL2. Further, the number of employed sensors in the divided area at the left end is assumed to be DR and DL. Then, by substituting those values into the above formulas (2) and (3), the peak selection start addresses PSR and PSL and the peak selection end when each region (2) to (7) is set as the peak selection region Addresses PER and PEL can be calculated. As shown in FIG. 22, in the case of region (2), PSR = 6, PER = 227, PSL = 8, PEL = 229, and in case of region (3), PSR = 86, PER. = 147, PSL = 88, PEL = 149, in the case of region (4), PSR = 46, PER = 107, PSL = 48, PEL = 109, in the case of region (5), PSR = 126, PER = 187, PSL = 128, PEL = 189, in case of region (6), PSR = 6, PER = 107, PSL = 8, PEL = 109, in case of region (7), PSR = 126, PER = 227, PSL = 128, and PEL = 229.

  In addition, the address of the cell at the right end of each divided area in the R sensor 94 is RSR, RMSR, MSR, LMSR, LSR, and the adopted sensor for each area in the order of the right area, the middle right area, the center area, the middle left area, and the left area The number is RWR, RMWR, MWR, LMWR, LWR, and the address of the rightmost cell of each divided area in the L sensor 96 is RSL, RMSL, SL, LMSL, LSL, and the number of employed sensors in each area is RWL, RMWL, When MWL, LMWL, and LWL are used, as shown in FIG. 23, when each region (1) to (7) is set as a peak selection region, the number of addresses and sensors to be substituted for SR1, SR2, SL1, SL2, DR, and DL Corresponds.

  As described above, when the peak selection start address PS and peak selection end address PE of the area to be the peak selection area are obtained, the block numbers D0 to D55, D56 having four cells as one block as shown in FIG. The range of the peak selection region is obtained by ~ D111. At this time, the four cells having the block numbers including the peak selection start address PS and the peak selection end address PE are cells in the peak selection area.

Therefore, for each of the L sensor 96 and the R sensor 94, the block numbers at the left end of the peak selection region are the peak selection start block numbers DSL and DSR, and the block numbers at the right end of the peak selection region are the peak selection end block numbers DEL and DER. ,
[Equation 4]
DSL = INT ((229−PEL) / 4) (4)
DEL = 55-INT ((PSL-6) / 4) (5)
DSR = 56 + INT ((229-PER) / 4) (6)
DER = 111-INT ((PSR-6) / 4) (7)
Thus, DSL, DEL, DSR, and DER are obtained. However, DSL = 0 if DSL <0, DEL = 55 if DEL> 55, DSR = 56 if DSR <56, and DER = 111 if DER> 111. .

  The peak selection area is in the range from the block number DSR to DER for the R sensor 94, and the range from the block number DSL to DEL for the L sensor 96. Therefore, the peak selection area setting data D0 to D111 are Is set to 1 in this range, and is set to 0 in other ranges.

At this time, if the number of peak selection regions is DPS, the number of peak selection regions DPS is expressed by the following equation:
[Equation 5]
DPS = DEL-DSL + 1 + DER-DSR + 1 (8)
Is obtained. The peak selection area number data D112 to D118 are obtained by expressing DPS by binary numbers.

  FIG. 24 shows peak selection region settings generated by the above equations (4) to (8) from the numerical example shown in FIG. 22 when each region (1) to (7) in FIG. 13 is a peak selection region. It is the figure which showed data D0-D111 and peak selection area number data D112-D118. For example, when the region (1) is a peak selection region, the peak selection start addresses PSR and PSL are 46 and 48, respectively, and the peak selection end addresses PER and PEL are 187 and 189, respectively. Substituting into the equations (4) to (7), the peak selection start block numbers DSL and DSR are 10 and 66, respectively, and the peak selection end block numbers DEL and DER are 45 and 101, respectively. Therefore, as shown in the peak selection region setting data D0 to D111 for the region (1) in FIG. 24, D0 to D9 is 0, D10 to D45 is 1, D46 to D55 is 0, D56 to D65 is 0, D66 to D101 is 1, and D102 to D111 are 0. Further, according to the above equation (8), the number of peak selection regions in the region (1) is 112, and as shown in the peak selection region number data D112 to D118 for the region (1) in FIG. 1001000.

  As described above, when the areas (1) to (7) are set as the peak selection areas, the peak selection area setting data D0 to D111 and the peak selection area number data D112 to D118 are addresses indicating the ranges of the divided areas. Since information is generated as reference data, it is not necessary to register a huge amount of data as shown in FIG. 24 in advance in the memory, and the memory can be saved. In the above-described embodiment, the case where the right end address of each divided area and the number of employed sensors are referred to as address information indicating the range of each divided area has been described. However, each divided area is referred to as address information indicating the range of each divided area. Peak selection region setting data D0 to D111 and peak selection region number data D112 to D118 can also be generated using the addresses at the right and left ends of the area as reference data. In addition, if the address information indicates the range of each divided area, the peak selection region setting data D0 to D111 and the peak selection region number data D112 to D118 can be generated even with address information other than the above.

  Next, processing when the / AFEND signal is not normally output from the AF sensor 74 will be described. For example, in each integration process in step S52, step S56, and step S58 in FIG. 14, normally, as shown in FIG. 17, after the / AFRST signal is switched from 0 to 1 (see time T40), a predetermined time has elapsed. The integration is started after 150 μs when the sensor sensitivity is high and after 30 μs when the sensor sensitivity is low, and the / AFEND signal is switched from 1 to 0 (see time T50). When the peak value of the sensor data in the peak selection region reaches the integration end value, the / AFEND signal is switched from 0 to 1 (see time T60). The CPU 60 detects the change of the / AFEND signal from 0 to 1 as an integration end signal, and recognizes the end of integration.

  On the other hand, if the subject brightness is low, if the subject brightness exceeds a certain brightness, or if a connection error of the / AFEND signal occurs, the / AFEND signal is normal even if the maximum allowable integration time has elapsed. May not be output.

  The reason why the / AFEND signal is not normally output when the subject brightness is low (the / AFEND signal does not switch from 0 to 1) is that the peak value of the sensor data does not reach the integration end value. For example, when the subject is bright, as shown in FIG. 26 (A), after the / AFEND signal is switched from 1 to 0 (after the integration process is started), the peak is reached before the maximum allowable integration time elapses. Since the peak value of the sensor data in the selected area reaches the integration end value (see the MDATA signal in the figure), the / AFEND signal is switched from 0 to 1 (an integration end signal is output). On the other hand, when the subject is dark, as shown in FIG. 26C, after the / AFEND signal is switched from 1 to 0 and before the maximum allowable integration time elapses, Since the peak value does not reach the integration end value (see the MDATA signal in the figure), the / AFEND signal is not switched from 0 to 1 until the maximum allowable integration time elapses, and the integration end signal is not output. When the maximum allowable integration time is reached, the CPU 60 gives a signal for forcibly terminating the integration process to the AF sensor 74 (AFAD = “L” in FIG. 26B). When the time elapses, the integration process ends, and the / AFEND signal switches from 0 to 1.

  On the other hand, when the subject luminance exceeds a certain luminance, the / AFEND signal is not normally output (the / AFEND signal does not switch from 1 to 0, and the / AFEND signal does not switch from 0 to 1). This is a problem on the characteristics of the sensor 74. That is, if the integration process is normally performed and the peak value of the sensor data reaches the integration end value, and the subject brightness is extremely high, the / AFEND signal may not be output normally due to the characteristics of the AF sensor 74. For example, the / AFEND signal may have the following output form (particularly, it is likely to occur during gain high-sensitivity integration in step S32 in FIG. 14). When the subject luminance exceeds a certain luminance, as shown in FIG. 25, the / AFEND signal should not be switched to 0 at the start of integration after a predetermined time has elapsed since the / AFRST signal was switched from 0 to 1. In the case of FIG. In this case, as a matter of course, the / AFEND signal does not switch from 0 to 1 even at the end of the integration process, and the integration end signal is not output. On the other hand, if the subject brightness is higher than this, the / AFEND signal is normally output (in the case of FIG. 5B), but the integration time is extremely short, so the / AFEND signal is changed from 1 to 0. There are cases where switching and switching from 0 to 1 cannot be recognized normally. If the subject brightness further increases and the sensor operation limit is exceeded, even if the integration is completed while the / AFEND signal is switched from 1 to 0, it will not be switched to 1 (in the case of (C) in the figure). No signal is output.

Thus, when the subject brightness exceeds a certain brightness (particularly when integration is executed with high sensitivity (S52)), as the brightness increases,
(a) The / AFEND signal does not switch from 1 to 0.
(b) The / AFEND signal switches from 0 to 1 after the / AFEND signal switches from 1 to 0.
(c) The / AFDEN signal changes from 1 to 0, but the / AFEND signal does not change from 0 to 1 thereafter.
In some cases, the / AFEND signal may not be output normally.

  When the / AFEND signal does not switch from 1 to 0, it is assumed that the luminance exceeds a certain luminance (in this case, a connection error of the / AFEND signal may be considered), and a certain luminance is actually obtained by MDATA described later. Judge whether it was over.

  Also, after the subject brightness becomes higher than a certain brightness and the / AFEND signal is switched from 1 to 0, the / AFEND signal is switched from 0 to 1 very quickly, and the / AFEND signal is normal in the CPU. Even if it cannot be recognized, it is assumed that the above-mentioned / AFEND signal does not switch from 1 to 0 when viewed from the CPU side. Therefore, it is estimated that the signal exceeds a certain luminance. Determine if it was over.

  If the subject brightness further increases, the / AFEND signal changes from 1 to 0, and then the / AFEND signal does not switch from 0 to 1, then the subject brightness is low and the integration has just finished at the maximum allowable integration time. In the case of high luminance, it is impossible to determine whether the / AFEND signal does not switch from 0 to 1 (because both the sensor peak (MDATA value shown later) is the minimum value).

  Therefore, if it is determined in step S50 in FIG. 14 that the object to be measured is extremely bright by the photometric sensor, step S52 (high sensitivity integration) in FIG. 14 is not performed, and the three-division gain low integration in step S56 is performed. It will be implemented to prevent the above misjudgment.

  Incidentally, there may be a case where the / AFEND signal does not switch from 1 to 0 due to a circuit failure.

here,
(A) In the case of a connection error with only the / AFEND signal (when integration is normally performed but integration start and integration end cannot be determined)
And (b) In the case of an integration operation error of the circuit (a connection error other than the / AFEND signal, and integration is not performed due to any connection error of V _CC , GND, / AFCEN, / AFRST, AFCLK, and AF (If integration is not performed due to sensor damage, etc.)
Can be considered.

  Also in the case of a connection error of the / AFEND signal, the / AFEND signal is not switched from 1 to 0, and the / AFEND signal is not normally output. For this reason, when the / AFEND signal does not switch from 1 to 0, not only the case where the subject luminance exceeds a certain luminance as described above, but also the case where a connection error of the / AFEND signal is assumed. And it is judged by MDATA shown later whether sensor data satisfy | fills integration completion conditions. / AFEND signal connection error and MDATA value is equal to or greater than MC_JDG (almost initial value (VREF)) in FIG. 26B (subject luminance is extremely low and signal accumulation is hardly performed. ) Is determined to be impossible.

  Also, in the case of a connection error of the / AFEND signal, if the value of MDATA is less than MC_JDG in FIG. 26B (when the subject brightness is not extremely low and signal accumulation is performed to some extent), distance measurement Continue.

  Also in the case of an integration operation error of the circuit, the / AFEND signal is not switched from 1 to 0, and the / AFEND signal is not normally output. Therefore, when the / AFEND signal does not switch from 1 to 0, not only the case where the subject luminance exceeds a certain luminance as described above, but also the case where an integration operation error of the circuit is assumed. And it is judged by MDATA shown later whether sensor data satisfy | fills integration completion conditions. In the case of an integration operation error of the circuit, the value of MDATA is almost the initial value (VREF) (integration is not performed). In this case, it is determined that distance measurement is impossible.

  On the other hand, if the subject luminance exceeds a certain luminance, the value of MDATA is about 0.6 V (integration is completed), and in this case, distance measurement is continued.

  For the sake of expression, it is assumed that the case where the sensor data becomes insufficient in signal amount due to low subject luminance belongs to the category where the integration processing is not normally performed. Also, it is judged that distance measurement is impossible due to insufficient signal amount when the peak value of sensor data has not changed at all since the start of integration processing or when there has been very little change. In other cases, it may be possible to measure the distance, so that it is determined that the integration process is normally performed, and it is not determined that the distance measurement is impossible.

  As described above, if the integration start signal or the integration end signal is not normally output from the AFEND signal even if the integration time has passed a fixed time, whether or not the integration processing is normally performed is determined by the MDATA signal. Judgment is made. Since the MDATA signal outputs the peak value of the sensor data in the peak selection region as analog data, as long as integration processing is performed, the / AFEND signal may not be output normally as shown in FIG. The peak value of the sensor data in the peak selection region is normally output from the MDATA signal, and it can be easily determined whether the integration process is normally performed.

  The processing contents of the CPU 60 will be specifically described. At the time of executing the collective gain high integration in step S52 of FIG. 14, the CPU 60 switches the / AFRST signal from 0 to 1 (see time T40 in FIG. 17), and then for a predetermined time. If the switch from AF sensor 74 indicating the start of integration / switching of AFEND signal from 1 to 0 is not detected by the time (for example, 500 μs), MDATA signal is read. If the value of the MDATA signal has reached the integration end value, the reason why the / AFEND signal has not been switched from 1 to 0 is that the subject brightness is high (super high brightness). In the same manner as in the case where the integration time is less than 2 ms, the process proceeds to the 3-division gain low integration process (see steps S54 and S56 in FIG. 14). On the other hand, when the value of the MDATA signal is equal to or greater than the predetermined value, that is, when there is no change from the value at the start of integration (in the case of the value of the reference voltage VREF), or a value that can be regarded as equivalent to no change at all. In this case, distance measurement is impossible as an integration operation error of the circuit. In cases other than the above, the integration process is continued as usual. Since the subsequent processing has been described with reference to the flowchart of FIG.

  When executing the three-division gain low integration in step S56 of FIG. 14 (when executing the integration processes of steps S84, S94, S102, S110, and S118 of FIG. 15), the CPU 60 outputs the / AFRST signal in the same manner as described above. After switching from 0 to 1, when a certain time (for example, 500 μs) elapses, the MDATA signal is read when the AF sensor 74 detects the start of integration / switching of AFEND signal from 1 to 0 is not detected. . At this time, when the value of the MDATA signal is greater than or equal to a predetermined value (MC_JDG in FIG. 26B), that is, when there is no change from the value at the start of integration (in the case of the value of the reference voltage VREF), or at all In the case of a value that can be regarded as equivalent to no change, distance measurement is disabled for the divided areas constituting the peak selection region in the integration process at that time (circuit integration operation error). On the other hand, when the AF sensor 74 detects the start of integration / switching of the AFEND signal from 1 to 0 before the fixed time elapses, the integration process is continued up to the maximum allowable integration time. If the AF sensor 74 indicates the end of integration / no change of the AFEND signal from 0 to 1 is detected before the maximum allowable integration time elapses, the MDATA signal is output when the maximum allowable integration time is reached. read. At this time, when the value of the MDATA signal is greater than or equal to a predetermined value (MC_JDG in FIG. 26B), that is, when there is no change from the value at the start of integration (in the case of the value of the reference voltage VREF), or at all In the case of a value that can be regarded as equivalent to no change, distance measurement is disabled (sensor data is invalidated) for the divided areas that constitute the peak selection region in the integration process at that time because the signal amount of sensor data is insufficient. In other cases, it is determined that the integration process has been normally performed, the sensor data accumulated until then is validated, and the sensor data at that time is read out. If the value of the MDATA signal does not reach the integration end value even after the maximum allowable integration time has elapsed, the integration process continues, so the CPU 60 forcibly stops the integration process of the AF sensor 74. (After switching the AFAD signal from 1 to 0), the sensor data is read out.

  When executing the collective gain low integration in step S58 in FIG. 14, the CPU 60 integrates from the AF sensor 74 after a certain time (for example, 500 μs) has elapsed after switching the / AFRST signal from 0 to 1, as described above. If the change of the AFEND signal indicating the end from 0 to 1 is not detected, the MDATA signal is read. At this time, when the value of the MDATA signal is greater than or equal to a predetermined value, that is, when there is no change from the value at the start of integration (in the case of the value of the reference voltage VREF), it can be regarded as equivalent to no change. In the case of a value, it is determined that distance measurement is impossible (circuit integration operation error). In the collective gain low integration, since the peak selection region is set in the entire distance measurement area, distance measurement is impossible in all the divided areas constituting the distance measurement area (range measurement itself is impossible). On the other hand, when the AF sensor 74 detects the start of integration / switching of the AFEND signal from 1 to 0 before the fixed time elapses, the integration process is continued up to the maximum allowable integration time. If the AF sensor 74 indicates the end of integration / no change of the AFEND signal from 0 to 1 is detected before the maximum allowable integration time elapses, the MDATA signal is output when the maximum allowable integration time is reached. read. At this time, when the value of the MDATA signal is equal to or greater than a predetermined value, that is, when there is no change from the value at the start of integration (in the case of the value of the reference voltage VREF), or a value that can be regarded as equivalent to no change at all. In this case, the distance measurement is impossible because the signal amount of sensor data is insufficient (sensor data is invalidated). In other cases, it is determined that the integration process has been normally performed, the sensor data accumulated until then is validated, and the sensor data at that time is read out. In this case as well, as described above, if the value of the MDATA signal does not reach the integration end value even after the maximum allowable integration time has elapsed, the integration process continues, so the CPU 60 forces the AF sensor. After the integration process 74 is stopped (after the AFAD signal is switched from 1 to 0), the sensor data is read out.

  Next, sensor data reading processing by the CPU 60 will be described. As shown in the timing chart of FIG. 17, for example, when the CPU 60 detects that the / AFEND signal transmitted from the AF sensor 74 is switched from 0 to 1 and integration is completed, the AFCLK signal is sent to the AF sensor 74. A clock pulse is transmitted by the signal, and reading of the sensor data is started. From the AF sensor 74, sensor data for each cell is sequentially output as analog data by the AFDATAP signal in synchronization with the clock pulse, and after A / D conversion, is input to the CPU 60.

  Specifically, the sensor data of each cell of the L sensor 96 and the R sensor 94 is alternately output in order from the sensor number 1 to the sensor number 234 by the AFDATAP signal, and is read by the A / D conversion circuit of the CPU 60. In addition, after the sensor data of all the cells of the L sensor 96 and the R sensor 94 are transmitted, several pieces of dummy data are transmitted.

  Here, the reading speed of the sensor data by the clock pulse will be described. As described above, when a certain peak selection region is set for the AF sensor 74 and the integration process is executed, the CPU 60 subsequently executes sensor data of each cell accumulated in the AF sensor 74 by the integration process. The sensor data actually used in the distance measurement calculation process is limited to the sensor data of each cell within the range of the peak selection region constituted by one or a plurality of divided areas, and the sensor of each cell in the other range No data is required. In addition, even within the range of the peak selection area as described above, the peak selection area setting for the AF sensor 74 is performed in block units (block numbers D0, D1,... D55, block numbers D56, D57,..., D111)). Actually, there is a cell that does not need to acquire sensor data even in the blocks at both ends of the peak selection region. Furthermore, in the present embodiment, the necessary sensor data is limited to the peak selection area, but depending on the mode of distance measurement, not only the peak selection area but also the sensor data of cells outside the peak selection area is necessary. There is. Therefore, a cell range that generates sensor data necessary for the subsequent processing of the CPU 60 is referred to as a data acquisition range, and a cell range that generates unnecessary sensor data in the subsequent processing of the CPU 60 is referred to as a data non-acquisition range. , The CPU 60 does not output a clock pulse with a fixed period, but as shown in FIG. 27, the sensor data of the cells in the data non-acquisition range than the clock period of the period T2 for carrying the sensor data of the cells in the data acquisition range as shown in FIG. The clock period of the period T1 for conveying the data is shortened, and the reading time of unnecessary sensor data is shortened.

  For example, when transporting sensor data of cells in the data acquisition range, if the period (“H”) of the AFDATAP signal is 16 μs and the period of the A / D conversion period is 18 μs (“L”), When the sensor data of cells in the data non-acquisition range is conveyed, the clock cycle is set to (“H”) 2 μs and (“L”) 2 μs. With a clock pulse having a period of (“H”) 16 μs and (“L”) 18 μs, the sensor data value of each cell can be appropriately acquired by the A / D conversion circuit. There is a possibility that the value of the sensor data of the cell cannot be acquired appropriately. However, since sensor data in the data non-acquisition range is unnecessary, there is no problem in transporting the sensor data in the data non-acquisition range with clock pulses having a cycle of (“H”) 2 μs and (“L”) 2 μs.

  Further, as shown in FIG. 27, when the transfer of all the sensor data in the data acquisition range is completed, the AFCLK signal (clock pulse) is output even when the transfer of the sensor data in the non-data acquisition range remains. By stopping and not reading the sensor data in the non-data acquisition range after the data acquisition range, it is possible to further shorten the sensor data read time.

  In the case of shifting from the transport of sensor data in the data non-acquisition range to the transport of sensor data in the data capture range, one of the start of transport of sensor data in the data capture range is considered in consideration of clock pulse stability and the like. The clock cycle is switched to (“H”) 16 μs and (“L”) 18 μs from the previous cell. However, the shift to the clock cycle during the transfer of the sensor data in the data acquisition range is not performed from the cell immediately before the start of the transfer of the sensor data in the data acquisition range, but the sensor data in the data acquisition range. It may be performed simultaneously with the start of the transfer of the above, or may be performed from two or more previous cells.

  Next, a process for generating AF data from sensor data will be described. As described above, when the data output from the light receiving cell of the AF sensor 74 is sensor data, each sensor data output from the AF sensor 74 is acquired by an A / D conversion circuit, and A / D conversion of the acquired sensor data is performed. A case where the value itself is used as AF data to be used in each subsequent process in the CPU 60, and a case where sensor data is subjected to a predetermined process for improving the distance measurement accuracy are considered as AF data. In the former case, it is not necessary to perform a special process for generating AF data in the CPU 60, and the sensor data acquisition process is the AF data acquisition process. In the latter case, after the sensor data is acquired, the CPU 60 Special processing for generating AF data is performed in step S2. As an example of the latter case, the sensor data subjected to contrast extraction processing can be used as AF data to be used in the subsequent processing. Hereinafter, the sensor data is subjected to contrast extraction processing to generate AF data. The process will be described.

In contrast extraction processing, for example, when a cell of a certain sensor number (address i) is focused, the sensor data of the focused cell and a sensor number (m pixels) apart from the focused cell are extracted. i + m) is a calculation process for calculating a difference (or ratio) from the sensor data of the cell. In other words, for each of the sensor data obtained from the R sensor 94 and the L sensor 96, the difference between the sensor data and the sensor data shifted by m pixels is calculated. That is, if the sensor data of the cell of sensor number (i) in the R sensor 94 is R (i) and the sensor data of the cell of sensor number (i) in the L sensor 96 is L (i), the sensor data of the R sensor 94 Is given by
[Equation 6]
R (i) -R (i + m) (9)
For the sensor data of the L sensor 96, the following equation is given:
[Equation 7]
L (i) -L (i + m) (10)
Is calculated. The difference data thus obtained indicates the contrast of the sensor image captured by each cell of the AF sensor 74. In the present specification, an arithmetic process for calculating data indicating contrast based on a difference between sensor data for two pixels is referred to as a two-pixel difference calculation.

  The value of the cell interval m of the two sensor data taking the difference can be a desired set value, but m = 2 in the following description. However, in the AF sensor 74, the charge accumulated in the cells with even sensor numbers and the charge accumulated in the odd cells are transmitted and processed by different channels. It is preferable to obtain from data, and it is desirable that the value of m is an even number. The data obtained by the above equations (9) and (10) is reduced by m compared to the number of sensor data acquired from the AF sensor 74 by the CPU 60, but considering that it is reduced by m in advance. The necessary number of AF data can be secured by expanding the data acquisition range.

Conventionally, the difference data obtained by the above formulas (9) and (10) is used as AF data, but in this embodiment, a process of adding +255 to the difference data and a process of dividing by 2 AF data is added. That is, when AF data corresponding to the sensor number i of the R sensor 94 is AFR (i) and AF data corresponding to the sensor number i of the L sensor 96 is AFL (i), when m = 2,
[Equation 8]
AFR (i) = (255 + R (i−1) −R (i + 1)) / 2 (11)
AFL (i) = (255 + L (i−1) −L (i + 1)) / 2 (12)
The value obtained by the above is AF data.

  Here, the AF data is not simply the difference data obtained by the above equations (9) and (10), but the above equations (11) and (12) are used to increase the RAM usage and the correlation value. This is to prevent an increase in calculation time such as calculation. For example, it is assumed that sensor data of each cell is obtained as 8-bit data. In this case, the values of the sensor data R (i) and L (i) are in the range of 0 to +255 as shown in FIG. On the other hand, when the difference data obtained by the above equations (9) and (10) is used as AF data (this case is referred to as a conventional method), the value of the AF data is −255 as shown in FIG. It is in the range of ~ + 255 and becomes 9-bit data. Since the RAM is used and calculated in units of bytes, 9-bit data is processed as 16-bit (2 bytes) data.

  On the other hand, when the difference data obtained by the above equations (11) and (12) is used as AF data (this case is referred to as a new method), as shown in FIG. It is in the range of +255 and becomes 8-bit data. Therefore, in the use and calculation of the RAM, it is processed as 1-byte data. FIGS. 29A and 29B illustrate AF data values generated by the new method based on the same sensor data, and AF data values generated by the conventional method, respectively.

  By generating AF data so that it has the same number of bits as sensor data as in the new method, the RAM usage is reduced and the processing time in each subsequent process such as correlation value calculation is reduced. In addition, in the new method based on the above formulas (11) and (12), the difference calculation result of the conventional method based on the above formulas (9) and (10) is halved. It has been confirmed that there is no problem.

  Next, specific processing contents when generating AF data by the new method will be described. Conventionally, when sensor data of each cell in the ranging area is read from the AF sensor, the read sensor data is stored in the RAM as it is. When processing such as correlation value calculation using AF data (image) is started, AF data (image) is read from the RAM during execution of the processing, and the above formulas (9) and (10) Necessary difference data is sequentially generated by calculation. For example, in the correlation value calculation process in which AF data (image) is used, as shown in FIG. 12, the shift amount n (the number of employed sensors 62, while the R window 94B and the L window 96B are shifted one cell at a time). In the case of the window size 42, the correlation value is calculated from the AF data (image) in each window range for every n = −2, −1, 0, 1,..., MAX (= 38)). For this reason, the AF data (image) of the same cell is repeatedly used, and the AF data (image) is read from the RAM each time, and difference data is generated by 2-pixel difference calculation. Thus, the correlation value for each shift amount n (n = −2, −1, 0, 1,..., MAX (= 38)) when differential data is generated during the execution of the correlation value calculation (during execution). The calculation processing procedure is shown in FIG. First, i = 1 and sensor data L (i + 1) and L (i-1) are read from the RAM (steps S600 and S602). Note that i shown here indicates the cell position i in the R window 94B and the L window 96B. Then, the difference data AFL (i) = (255 + L (i−1) −L (i + 1)) / 2 is calculated by the above equation (12) (step S604). Similarly, sensor data R (i + 1) and R (i-1) are read from the RAM (steps S606 and S608). Then, the difference data AFR (i) = (255 + R (i−1) −R (i + 1)) / 2 is calculated by the above equation (11) (step S610). Next, let | L (i) −R (i) | on the right side in the above equation (1) for obtaining the correlation value f (n) be f (ni), and whether AFL (i)> AFR (i). Is determined (step S612). If YES is determined, f (ni) = L (i) −R (i) is set (step S614). If NO is determined, f (ni) = R (i) −L (i) is set (step S614). S616). Subsequently, f (ni) is added to the value (initial value 0) of the left side f (n) of the above formula (1), and the value is set as a new value of f (n). That is, f (n) = f (n) + f (ni) is set (step S618).

  Next, it is determined whether or not i = (window size wo (= 42)) (step S620). If it is determined YES, this process ends (step S622). On the other hand, if NO is determined, i = i + 1 is set (step S624), the process returns to step S600, and the process is repeated from step S600. The above is the calculation for each shift amount n, and the calculation process is repeated from i = 1 to i = wo for each shift amount n (n = −2 to 38).

  As described above, when the difference data is generated during the execution of the correlation value calculation process using the AF data (image), the AF data (image) of the same cell is repeatedly used as shown in FIG. It is necessary to execute pixel difference calculation. Therefore, the calculation takes time, resulting in a problem that the time required for the distance measurement calculation becomes long.

  In order to solve the above problem, in the present embodiment, AF data (difference) is generated in advance before starting processing using difference data, and the generated AF data (difference) is stored in the RAM. To leave. FIG. 31 shows a correlation value calculation processing procedure when AF data (difference) is generated and stored in the RAM before the correlation value calculation is executed. First, AF data (difference) AFL (i) and AFR (i) are read from the RAM with i = 1 (steps S650 and S652). Note that i shown here indicates the cell position i in the R window 94B and the L window 96B. Next, let | L (i) −R (i) | on the right side in the above equation (1) for obtaining the correlation value f (n) be f (ni), and whether AFL (i)> AFR (i). Is determined (step S654). If YES is determined, f (ni) = L (i) −R (i) is set (step S656). If NO is determined, f (ni) = R (i) −L (i) is set (step S656). S658). Subsequently, f (ni) is added to the value (initial value 0) of the left side f (n) of the above formula (1), and the value is set as a new value of f (n). That is, f (n) = f (n) + f (ni) is set (step S660).

  Next, it is determined whether or not i = (window size wo (= 42)) (step S662). If it is determined YES, this process ends (step S664). On the other hand, if NO is determined, i = i + 1 is set (step S666), the process returns to step S650, and the process is repeated from step S650.

  By generating AF data (difference) in advance and storing it in the RAM in this way, it is only necessary to read out the necessary AF data (difference) from the RAM when executing each process using the AF data (difference). The time required for processing for generating the difference data is greatly reduced. When the processing time in the correlation value calculation shown in FIG. 30 and FIG. 31 is compared, in the processing of FIG. The time required for value calculation can be shortened. The number of i calculations is (i = 1 to 42 → 42 times) × (n = −2 to 38 → 41 times) × 5 area = 42 when the number of sensors used is 62 and the window size 42 is set to 5 areas. × 41 × 5 = 8610 times. Accordingly, the total distance measurement time can be shortened by 8610 times × (2 × 21 μs) ≈362 ms. When AF data (difference) is generated in advance and stored in the RAM, a processing time for generating AF data (difference) from the sensor data is required separately from the time for calculating the correlation value. This will be described later.

  By the way, there are two possible modes for performing the two-pixel difference calculation. In the first aspect, the sensor data read from the AF sensor 74 is temporarily stored in the RAM (AF data (image)), and then the AF data (image) is read from the RAM to obtain the difference data by the above formula (11), The two-pixel difference calculation is performed by (12). In the second mode, when the sensor data is sequentially read from the AF sensor 74, the sensor data necessary for the calculations of the above equations (11) and (12) is obtained for the sensor data of each sensor number i. The difference calculations (11) and (12) are performed, and the difference calculation results generated sequentially are stored in the RAM (AF data (difference)). The sensor data reading process in the first mode is performed independently of the difference data generation process, and therefore AF data (image) is used when the two-pixel difference calculation is not performed or when each process such as correlation value calculation is performed. This is the same as the sensor data reading process when generating. On the other hand, in the sensor data reading process of the second aspect, since the two-pixel difference calculation is performed while reading the sensor data, the time required for reading the sensor data increases accordingly. However, in consideration of performing the two-pixel difference calculation also in the first mode, the first mode is not more advantageous than the second mode.

  Here, FIG. 32 shows the flow of data when differential data is generated during correlation value calculation or the like (hereinafter referred to as the conventional method), and AF data (difference) is generated in advance and stored in the RAM. FIG. 33 shows a data flow in the case where the second mode is adopted as a case (hereinafter, this case is referred to as a new method). As shown in FIG. 32, in the conventional method, the sensor data of each cell sequentially read from the AF sensor is stored in the RAM. Then, at the time of executing the correlation value calculation, AF data (image) is read from the RAM, difference data is generated by the above equation (11) or (12), and a correlation value f (n) is calculated. On the other hand, as shown in FIG. 33, in the second mode of the new method, the sensor data of each cell sequentially read out from the AF sensor is subjected to difference calculation processing by the above formula (11) or (12) and the AF data ( Difference) is stored in the RAM. When executing the correlation value calculation, the AF data (difference) stored in the RAM is read to calculate the correlation value f (n). Although not shown in the figure, when converting sensor data into AF data (difference) using the above formula (11) or (12) in the new method, two sensor data are required, so two sensor data are read out. A memory (RAM) that holds sensor data previously read from the AF sensor is necessary. However, a memory capacity for storing all sensor data is not required. Specifically, the sensor data is read in the order of L (i−1), R (i−1), L (i), R (i), L (i + 1), R (i + 1),. When the cell interval m of two sensor data is 2, a RAM for storing five sensor data is sufficient. For example, when L (i-1), R (i-1), L (i), R (i), and L (i + 1) are stored, AFL (i) in the above equation (12) is stored in the RAM. L (i−1), L (i + 1) obtained. When the AFL (i) is obtained, the sensor data of L (i-1) is no longer necessary. Therefore, the data is deleted, and then the sensor data R (i + 1) read from the AF sensor 74 is obtained. By storing in the erased address, AFR (i) in the above equation (11) can be obtained from R (i−1) and R (i + 1). In this manner, the five sensor data read from the AF sensor 74 are stored in the RAM, and when new sensor data is read, the sensor data read first is stored among the sensor data stored in the RAM. By erasing and storing the new sensor data in the RAM, AF data (difference) can be sequentially generated with a RAM having a small memory capacity.

  Subsequently, regarding the sensor data reading process, the case where the conventional method is adopted and the case where the second mode is adopted as a new method will be compared. FIG. 34 is a flowchart showing sensor data read processing in the conventional method, and FIG. 35 is a timing chart showing the AFCLK signal and AFDATAP signal at the time of sensor data read in the conventional method. With reference to these figures, the sensor data reading process in the conventional method will be described. First, the AFCLK signal is switched from “H” to “L” (step S700), and the AFDATAT signal indicating the sensor data is A / D converted. (Step S702). Then, the AFCLK signal is switched from “L” to “H” (step S704), and the sensor data R (i) or L (i) acquired by the A / D conversion is stored in the RAM (step S706). The above processing is repeated. The periods of the AFCLK signal “H” and “L” are, for example, 16 μs and 18 μs, respectively.

  On the other hand, FIG. 36 is a flowchart of the sensor data reading process in the new method (second mode), and FIG. 37 is a diagram showing the AFCLK signal and the AFDATAP signal at the time of reading sensor data in the new method. The sensor data reading process in the new method will be described with reference to these drawings. First, the AFCLK signal is switched from “H” to “L” (step S750), and the AFDATAT signal indicating the sensor data is A / D converted. (Step S752). Then, the AFCLK signal is switched from “L” to “H” (step S754), and the sensor data R (i) or L (i) acquired by the A / D conversion is stored in the RAM (step S756). Next, sensor data R (i-2) or L (i-2) is read from the RAM (step S6-5), and AF data (difference) AFR (i-1) or according to the above equation (11) or (12). AFL (i-1) is calculated (step S760), and the calculated AF data (difference) AFR (i-1) or AFL (i-1) is stored in the RAM (step S762). The above processing is repeated.

  As can be seen from the above-described sensor data read processing procedure of the new method and the conventional method, the new method requires more time to read one sensor data than the conventional method by the operation time (21 μs) of steps S758, S760, and S762. Since the processes of steps S758, S760, and S762 are performed when the AFCLK signal is “H”, as shown in FIG. 37, the “H” period of the AFCLK signal is longer than the conventional method for reading the sensor data. Is 37 μs. That is, considering only the sensor data read time, the new method is disadvantageous than the conventional method. However, when the reading of sensor data is compared with the overall processing time including, for example, correlation value calculation, the new method can complete the processing in a shorter time. An example of specific calculation is shown in the table of FIG. The table shows sensor data read time, correlation value calculation time per time, and total correlation value for the new method, the conventional method, and the case where 2 pixel difference calculation is not performed (conventional (2) method). Calculation time (41 times, 5 area setting) and total (sensor data read time + correlation value total calculation time) are shown. Also, the difference Δ (1) between the new method and the conventional method, and the new method and the conventional method. (2) A difference Δ (2) from the method is shown.

  The sensor data read time is {AFCLK signal (“H” time + “L” time)} × number of cells × 2 (R sensor 94 and L sensor 96). Since the two-pixel difference calculation cannot be performed in the sensor data reading period of 5 cells, (sensor data reading time for 5 cells according to the conventional method) + (sensor data reading time for the remaining cells according to the new method). Applying the numerical values used in the above description, (16 + 18) × 5 + (37 + 18) × ((229−6 + 1) × 2−5) = 24535 μs.

  On the other hand, as the correlation value calculation time per time, an actual measurement value is used in the case of the new method, and an actual measurement value + an increase in calculation time is used in the case of the conventional method. The increase in calculation time is 21 μs × 2 as shown in FIG. The correlation value calculation time per time in the conventional method is 1.2 × 0.021 × 2 × 42 = 2.964 ms.

  As can be seen from this table, in the new method, the reading time of all sensor data is about 9 ms longer than in the conventional method. However, with regard to correlation value calculation, the processing time is reduced by about 361 ms in the new method compared to the conventional method. Therefore, if the time required for other determination processing is the same, the distance measurement time is reduced by 352 ms in the new method. It will be.

  As described above, when the AF data is generated by performing the required processing on the sensor data, the generation of the AF data is performed by the CPU 60. To generate AF data, and the generated AF data may be given to the CPU 60. Further, a correlation value calculation process described later may be performed not by the CPU 60 but by the AF sensor 74, and a distance signal obtained as a result may be provided to the CPU 60.

{Details of correlation value calculation process (step S16 in FIG. 7)}
Next, the correlation value calculation process in step S16 of FIG. 7 will be described in detail. As described above with reference to FIG. 12, in the correlation value calculation process, the CPU 60 acquires the AF data in step S <b> 14 of FIG. 7 for each divided area constituting the distance measurement area of the R sensor 94 and L sensor 96 of the AF sensor 74. The correlation value f (n) (n = −2, −1, 0, 1,..., MAX (= 38)) is calculated by the above equation (1) based on the AF data acquired by the processing. Then, based on the calculated correlation value f (n), the shift amount n at which the correlation is highest for each divided area is detected. Note that the correlation value calculation process is not performed for the divided areas determined to have no contrast necessary for distance measurement in the contrast detection process 1 of step S14.

  Here, the CPU 60 performs a determination of f (n−1) ≧ f (n) <f (n + 1) in order to obtain the minimum value, and uses the correlation value f as the shift amount that provides the highest correlation (highest correlation). The shift amount n at which (n) is the minimum minimum value is detected. In many cases, the minimum value of the correlation value is one, and the shift amount of the highest correlation is the shift amount n at which the minimum value is obtained.

  On the other hand, there may be a plurality of local minimum values in the distribution of correlation values f (n) (judgment of f (n−1) ≧ f (n) <f (n + 1)). The amount is, in principle, a shift amount n at which a minimum minimum value (minimum minimum value) is obtained among a plurality of minimum values. However, when there are a plurality of local minimum values, there is a possibility of erroneous distance measurement. Therefore, in the local minimum value determination process described below, the shift amount n of the minimum local minimum value is adopted as the maximum correlation shift amount. Judge whether or not is appropriate. It should be noted that the minimum value and its shift amount when there is one minimum value, or the minimum minimum value and its shift amount when there are a plurality of minimum values, are both the minimum minimum value fmin1 (or the minimum minimum value f (nmin1)). ) And the shift amount nmin1.

  Next, the minimum value determination process will be described. When two or more minimum values of the correlation value f (n) exist in a certain divided area, the CPU 60 detects the minimum minimum value fmin1 and the second smallest minimum value (referred to as the second minimum value), and the difference between them is detected. (Minimum value difference) is obtained. The second minimum value is represented by fmin2, and the difference between the minimum values is represented by Δfmin (= fmin2−fmin1). As an outline of processing for determining the minimum value, when the minimum value difference Δfmin is large, the shift amount nmin1 of the minimum minimum value fmin1 is adopted as the shift amount n of the highest correlation. On the other hand, when the minimum value difference Δfmin is small, there is a high possibility of erroneous distance measurement, and therefore distance measurement is impossible. Hereinafter, the shift amount of the highest correlation is represented by nmin, and the correlation value at the highest correlation (maximum correlation value) is represented by fmin or f (nmin).

  By the way, when there are a plurality of minimum values of the correlation value f (n), whether or not distance measurement is possible is determined based on whether the minimum value difference Δfmin is large or small with respect to a certain reference value. Considering the following aspects, there is a problem that the distance measurement becomes unnecessarily impossible or the possibility of erroneous distance measurement becomes high. That is, there are a case where distance measurement should be possible even if the minimum value difference Δfmin is small to some extent, and a case where distance measurement should be impossible even if the minimum value difference Δfmin is large to some extent.

  For example, FIG. 39 shows an aspect in the former case, and FIG. 40 shows an aspect in the latter case. 39A and 39B show examples of AF data obtained from each cell in the central area of each R sensor 94 and L sensor 96 of the AF sensor 74. As shown in FIG. It is assumed that AF data obtained from each cell in the area has low contrast. In the following description, among the divided areas (each divided area constituting the distance measuring area) in which the same processing is performed, the divided area or its sensor (each cell) focused on the description is referred to as an adopted sensor. . In such a case, the correlation value f (n) calculated by the correlation value calculation is an overall small value (by comparison with FIG. 40 described later) as shown in FIG. In FIG. 6C, the minimum minimum value fmin1 is detected when the shift amount n = 8, and the second minimum value fmin2 is detected when the shift amount n = 18. This is smaller than the case of FIG. However, in the case of the mode of FIG. 39, the shift amount nmin1 (= 8) of the minimum minimum value fmin1 is a value that appropriately corresponds to the subject distance, and it should be determined that distance measurement is possible.

  On the other hand, FIGS. 40A and 40B show an example in which AF data obtained from the adopted sensors (center area) of the R sensor 94 and the L sensor 96 changes periodically. Such AF data is obtained, for example, when a striped subject is imaged. In this case, the correlation value f (n) calculated by the correlation value calculation also periodically changes as shown in FIG. 10C, and becomes a large value as a whole (by comparison with FIG. 39 described above). . Further, as shown in the enlarged view of FIG. 4D, the minimum minimum value fmin1 is detected at the shift amount n = 14, and the second minimum value fmin2 is detected at the shift amount n = 20. The minimum value difference Δfmin is also larger than in the case of FIG. However, in the case of the mode of FIG. 40, there is a high possibility that the shift amount nmin1 (= 14) of the minimum minimum value fmin1 does not appropriately correspond to the subject distance, and this adopted sensor should be unable to measure the distance. .

  Specifically, the CPU 60 performs a determination process as follows in order to appropriately determine the case where distance measurement should be possible as shown in FIG. 39 and the case where distance measurement should not be possible as shown in FIG.

First, the minimum minimum value fmin1 and the second minimum value fmin2 are detected among a plurality of minimum values detected in the distribution of the correlation values f (n) of the employed sensors. And the following formula:
[Equation 9]
fmin1 <reference value R3 (13)
Determine whether or not. This determination is a determination for dividing the cases of FIG. 39 and FIG. 40, and the reference value R3 is set to an appropriate value for dividing these cases. If the equation (13) is satisfied, that is, in the case of FIG. 39, the minimum value difference Δfmin (= fmin2−fmin1) is obtained, and the following equation:
[Equation 10]
Δfmin <reference value R2 (14)
Determine whether or not. In consideration of the case of FIG. 39, the reference value R2 is set to a value that is at least smaller than a reference value R1 described later. If equation (14) holds, it is determined that the minimum value difference Δfmin is small, and the distance measurement is not possible for this adopted sensor. If equation (14) does not hold, distance measurement is possible, and the shift amount nmin1 of the minimum minimum value fmin1 is adopted as the shift amount nmin of the maximum correlation value fmin.

On the other hand, when the above equation (13) does not hold, that is, in the case of FIG.
[Equation 11]
Δfmin <reference value R1 (15)
Determine whether or not. The reference value R1 is set to a value that is at least larger than the reference value R2 in consideration of the case of FIG. If equation (15) holds, it is determined that there is a high possibility that the subject is a striped pattern, as shown in FIG. If equation (15) does not hold, distance measurement is possible, and the shift amount nmin1 of the minimum minimum value fmin1 is adopted as the shift amount nmin of the maximum correlation value fmin.

  By performing the minimum value determination process as described above, the frequency of occurrence of problems such as erroneous distance measurement is reduced.

  Next, a plurality of other modes for shortening the distance measurement time in the correlation value calculation process will be described. First, a first embodiment for shortening the distance measurement time will be described. In the description of step S16 in FIG. 7 (see equation (1)), the correlation values f (n) (n = −2, −1, 0, 1,..., MAX (= 38)) are R window 94B and L window. The absolute value of the difference of AF data at the same cell position i (i = 1, 2,..., Wo (= 42)) of 96B (hereinafter simply referred to as AF data difference) is added for all cell positions i. . In the first embodiment, when obtaining the correlation value f (n) of each shift amount n, the difference between the AF data is not added for all the cell positions i, but the cell positions i are set at regular intervals. It shall be added. For example, for every third cell position i, the difference of AF data is added. In the following description, a calculation target cell to which the difference of AF data is added in the calculation of the correlation value f (n) is referred to as an adopted cell. Further, the correlation value calculation when the cells at every third cell position i are adopted cells is hereinafter referred to as “i every third cell calculation”. On the other hand, all the cell positions i are expressed as shown in Expression (1). Hereinafter, the correlation value calculation in the case where the selected cell is adopted is referred to as “normal calculation”.

  FIG. 41 is a diagram showing cell positions i of adopted cells in the R window 94B and the L window 96B in every third i calculation. As shown in the figure, in every third i-th computation, cells in every third cell position i (= 1, 5, 9, 13,...) Are selected as the adopted cells in order from the cell position 1 in both the R window 94B and the L window 96B. Adopted. When the window size is 42 (when wo = 42), the final cell position i of the adopted cells is 41. The equation for calculating the correlation value f (n) in this case is the following equation, as in the above equation (1):

Where i is taken every three as i = 1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41. Note that the cell positions i of the adopted cells need not be every three, and may not be from the cell position i = 1. In the above equation (16), a factor of four times is added to the above equation (1), but every third i3 operation has 1/4 the number of data compared to the normal operation. This is because of the combination.

  Here, examples of calculation results when the correlation value f (n) is obtained by the normal calculation and every third i3 calculation based on the AF data obtained in each cell of the employed sensor are shown in FIGS. 42 and 43, respectively. Show. As can be seen from these figures, the outline of the distribution of the correlation value f (n) is not much different from the normal calculation even in the case of every third i calculation, and in this example, the same as the normal calculation in the case of every third i calculation. The minimum minimum value is obtained with the shift amount n (= 10).

  When the CPU 60 calculates the correlation value f (n) for each shift amount n by every third i calculation as described above, the shift amount n at which the correlation value f (n) is a minimum value as in the normal calculation. Is detected. At this time, as can be seen from FIG. 42 and FIG. 43, the correlation value f (n) obtained by calculation every third i is more varied and less accurate than the correlation value f (n) obtained by normal calculation. Therefore, the position of the minimum value in every i3 calculation may be different from the position of the minimum value in the normal calculation. Therefore, when performing every third i calculation, if there is only one detected minimum value, that minimum value is set as the provisional minimum minimum value, and if there are a plurality of detected minimum values, the minimum minimum value among them is used. Is the provisional minimum value, and the correlation value f (n) is calculated again by normal calculation in the vicinity of the shift amount of the provisional minimum value. In the case where there are a plurality of detected minimum values, when the difference between the second smallest minimum value (provisional second minimum value) and the provisional minimum minimum value is small, in the vicinity of the shift amount of the provisional second minimum value. In this case, the correlation value f (n) is calculated by normal calculation. Details of this case will be described later. Further, hereinafter, the provisional minimum value is expressed as Tfmin1, and the shift amount at that time is expressed as Tnmin1. Further, a range in which the correlation value f (n) is recalculated by normal calculation is referred to as a recalculation range.

  When the CPU 60 recalculates the correlation value f (n) in the recalculation range near the shift amount Tnmin1 by the normal calculation, the minimum value of the correlation value f (n) and the shift amount in the recalculation range are calculated by the normal calculation. Detection is performed based on the correlation value f (n). The minimum value and shift amount detected by this recalculation are detected when all correlation values f (n) in the employed sensor are obtained by normal calculation except when it is determined that distance measurement is impossible (described later). Since it corresponds to the above-mentioned minimum minimum value fmin1 and the shift amount nmin1, it is represented by the minimum minimum value fmin1 and the shift amount nmin1, respectively. When emphasizing the detection by recalculation, it is referred to as a recalculation minimum minimum value fmin1. The recalculation minimum minimum value fmin1 and the shift amount nmin1 detected by this recalculation process are the highest correlation value fmin to be detected by the correlation value calculation process and the shift amount nmin, unless it is determined that distance measurement is impossible. Become.

  The recalculation range is, for example, a shift amount range of ± 5 with respect to the shift amount Tmin1 from which the provisional minimum value Tfmin1 is obtained. For example, in the case of FIG. 43, the shift amount Tminin1 from which the provisional minimum minimum value Tfmin1 is obtained is 10, so the recalculation range is the shift amount n = 5 to 15 as shown in FIG. When recalculation is performed in this recalculation range, the same correlation value f (n) as that in FIG. 42 is calculated in the recalculation range as shown in the figure. Therefore, the shift amount of the minimum minimum value fmin1 that should be detected originally nmin1 is detected by recalculation. In this example, the shift amount Tminin1 of the provisional minimum minimum value Tfmin1 and the shift amount nmin1 of the recalculated minimum minimum value fmin1 coincide with each other.

  Here, if the shift amount Tnmin1 of the temporary minimum value Tfmin1 detected by every third i-th operation is within the short distance warning range as shown in FIG. 45, the recalculation by the above-mentioned normal calculation is not performed, and Tnmin1 = It is set as a near distance warning as nmin1. The short distance warning range refers to a short distance range that cannot be focused in autofocus, and the above recalculation is performed only when the shift amount Tmin1 of the provisional minimum minimum value Tfmin1 is outside the short distance warning range.

  When the CPU 60 detects the shift amount nmin1 of the recalculation minimum minimum value fmin1 from the correlation value f (n) calculated by the recalculation as described above, the shift minimum nmin1 and the provisional minimum minimum value detected by every third i3 calculation. The shift amount Tnmin1 of Tfmin1 is compared. Normally, these shift amounts match, but in some cases they do not match. In this case, the correlation value f (n) (correlation value obtained by the normal calculation) in the necessary range in the subsequent processing such as the interpolation value calculation processing in step S22 in FIG. Additional recalculation of the value f (n) is performed. That is, the correlation value f (n) by the normal calculation is calculated in at least a certain shift amount range (for example, ± 5 range) with respect to the shift amount nmin1 (maximum correlation shift amount nmin) of the recalculation minimum minimum value fmin1. If the shift amount Tnmin1 of the provisional minimum minimum value Tfmin1 and the shift amount nmin1 of the recalculation minimum minimum value fmin1 do not match, it is necessary for the shift amount nmin1 of the recalculation minimum minimum value fmin1. The correlation value f (n) in the shift range is insufficient. In the present embodiment, the shift amount range in which the correlation value f (n) by the normal calculation is required for the shift amount nmin1 of the recalculation minimum minimum value fmin1 is the same range (± 5 range) as the recalculation range.

  Therefore, among the shift amount ranges necessary for the shift amount nmin1 of the recalculation minimum minimum value fmin1, the shift amount outside the recalculation range in which the correlation value f (n) by the normal calculation has already been calculated is correlated by the normal calculation. Additional recalculation of the value f (n) is performed. However, if the difference (shift amount difference nSA) between the shift amount Tminin1 of the provisional minimum minimum value Tfmin1 and the shift amount nmin1 of the recalculation minimum minimum value fmin1 is a predetermined value (for example, 3) or more, the recalculation minimum minimum value Since there is a high possibility that fmin1 is not the minimum minimum value that should be detected, distance measurement is impossible. It should be noted that the case where the minimum value is not detected as a result of the recalculation corresponds to this case and the distance measurement is impossible. The process for recalculating the deficient correlation value f (n) is referred to as deficient correlation value recalculation processing, and details thereof will be described later.

  Next, there are a plurality of minimum values in the correlation value f (n) obtained by every i3 calculation, and the minimum minimum value (provisional minimum minimum value Tfmin1) and the second minimum value (provisional second minimum value) among them A case where the difference is small will be described. For example, when the correlation value f (n) is calculated by normal calculation for all shift amounts n (n = −2 to MAX (= 38)) in the employed sensor, there are a plurality of minimum values as shown in FIG. It is assumed that the shift amount nmin1 of the minimum minimum value fmin1 is detected with a shift amount n = 7, and the shift amount nmin2 of the second minimum value fmin2 is detected with a shift amount n = 32. Then, when the correlation value f (n) is calculated for every such AF data by every i3 calculation, a correlation value distribution as shown in FIG. 47 is obtained. In this case, the CPU 60 first detects the minimum minimum value and the second minimum value in the distribution of the correlation value f (n) calculated by the calculation of every third i as shown in FIG. Note that, as described above, the minimum minimum value in every third i-th operation is expressed as the temporary minimum minimum value Tfmin1, the shift amount is expressed as Tnmin1, the second minimum value is expressed as the temporary second minimum value Tfmin2, and the shift amount is expressed as Tnmin2. In FIG. 47, the shift amount Tminin1 of the provisional minimum minimum value Tfmin1 is detected as 32, and the shift amount Tanmin2 of the provisional second minimum value Tfmin2 is detected as 6.

  If the difference between the provisional minimum minimum value Tfmin1 and the provisional second minimum value Tfmin2 (minimum value difference ΔTfmin = Tfmin2−Tfmin1) detected by every three i3 computations is equal to or greater than a predetermined reference value, as described above. The CPU 60 performs recalculation (normal calculation) only in the recalculation range for the shift amount Tnmin1 of the provisional minimum value Tfmin1. On the other hand, when the minimum value difference ΔTfmin is smaller than the reference value, recalculation is performed between the recalculation range for the shift amount Tnmin1 of the provisional minimum minimum value Tfmin1 and the recalculation range for the shift amount Tnmin2 of the provisional second minimum value Tfmin2. I do. However, when the minimum value difference ΔTfmin is smaller than the reference value, when both the shift amount Tminin1 of the provisional minimum minimum value Tfmin1 and the shift amount Tminin2 of the provisional second minimum value Tfmin2 are within the short distance warning range. The short distance warning is made without performing recalculation. If either one is not within the short distance warning range, recalculation is performed in the vicinity of both shift amounts as described above.

  FIG. 48 is a diagram showing the result of recalculation when it is determined that the minimum value difference ΔTfmin in FIG. 47 is smaller than the reference value. As shown in the figure, a shift amount range of ± 5 (shift amount n = 27 to 37) is recalculated as a recalculation range with respect to the shift amount Tminin1 (= 32) of the provisional minimum value Tfmin1, and the recalculation range The correlation value f (n) by the normal calculation is calculated at. Further, a shift amount range of ± 5 (shift amount n = 1 to 11) is recalculated as a recalculation range with respect to the shift amount Tminin2 (= 6) of the provisional second minimum value Tfmin2, and the normal range in the recalculation range A correlation value f (n) is calculated by calculation. The correlation value f (n) recalculated in these recalculation ranges is equal to the correlation value f (n) in the corresponding shift amount range in FIG.

  When the CPU 60 performs recalculation in the vicinity of the shift amount Tanmin1 of the provisional minimum minimum value Tfmin1 and the shift amount Tanmin2 of the provisional second minimum value Tfmin2 in this way, correlation by recalculation (normal calculation) is performed in those recalculation ranges. Based on the value f (n), the minimum value and its shift amount are detected. The detected minimum minimum value and the second minimum value are represented by a recalculated minimum minimum value fmin1 and a recalculated second minimum value fmin2, respectively, and their shift amounts are represented by a shift amount fmin1 and a shift amount fmin2, respectively. The recalculated minimum minimum value fmin1 and its shift amount fmin1, the recalculated second minimum value fmin2 and its shift amount nmin2 are all correlation values f ( The above-mentioned minimum minimum value fmin1 and its shift amount nmin1 detected when n) is obtained by normal calculation correspond to the second minimum value fmin2 and its shift amount nmin2, and the shift amount nmin1 of the minimum minimum value fmin1 is correlated. This is the shift amount nmin of the highest correlation value fmin to be detected by the value calculation process.

  Next, when there are a plurality of provisional minimum values, the shift amount Tminin1 of the provisional minimum value Tfmin1 detected by every i3 calculation and the shift amount nmin1 of the recalculation minimum value fmin1 detected by recalculation are obtained. The case where they do not match will be described. In this case, as described above, the correlation value f (n) by the normal calculation is required at least in a certain shift amount range (for example, a range of ± 5) with respect to the shift amount nmin1 of the recalculated minimum minimum value fmin1. Therefore, the recalculation (normal calculation) of the insufficient correlation value f (n) is additionally performed.

  Further, distance measurement is impossible when the shift amount difference nSA is equal to or larger than a predetermined reference value. In this case, the shift amount difference nSA is a shift of the recalculated minimum minimum value fmin1 to the temporary minimum minimum value Tfmin1. If it is detected due to the recalculation for the amount Tminin1, it is appropriate to make the difference between the shift amounts, but the shift amount nmin1 of the recalculation minimum minimum value fmin1 is the shift of the provisional second minimum value Tfmin2. When it is detected due to recalculation on the amount Tnmin2, it is appropriate to set the shift amount difference between the shift amount Tminin2 of the provisional second minimum value Tfmin2 and the shift amount nmin1 of the recalculation minimum minimum value fmin1.

  Therefore, the shift amount difference DIS1 (= | Tnmin1-nmin1 |) between the shift amount Tminin1 of the provisional minimum minimum value Tfmin1 and the shift amount nmin1 of the recalculation minimum minimum value fmin1, and the shift amount Tnmin2 of the provisional minimum minimum value Tfmin2 are recalculated. A shift amount difference DIS2 (= | Tnmin2−nmin1 |) of the minimum minimum value fmin1 and the shift amount nmin1 is obtained, and it is determined whether or not distance measurement is possible using the smaller value as the shift amount difference nSA. .

  In the case shown in FIG. 48, since the shift amount nmin1 of the recalculated minimum minimum value fmin1 is 7, the shift amount difference DIS1 is a value of 1 shown in (1) in the figure, and the shift amount difference DIS2 is (2) in the figure. It becomes 25 shown by. Accordingly, whether or not distance measurement is possible is determined by the magnitude of the shift amount difference DIS1.

  FIG. 49 is a flowchart showing the processing procedure of the shortage recalculation processing. Before executing the shortage recalculation process, the CPU 60 calculates the correlation value f (n) by every third i-th operation, and detects the shift amount Tnmin1 of the temporary minimum value Tfmin1. If there is a provisional second minimum value Tfmin2 other than the provisional minimum / minimum pole Tfmin1, the shift amount Tminin2 is detected. Then, recalculation by normal calculation is performed in the recalculation range, and the shift amount nmin1 of the recalculation minimum minimum value fmin1 is detected in the recalculation range.

  Subsequently, the process shifts to the shortage recalculation process of FIG. First, it is determined whether or not the shift amount Tnmin2 of the provisional second minimum value Tfmin2 exists, that is, whether or not the provisional second minimum value Tfmin2 exists (step S150). If NO, the process proceeds to step S168 described later. On the other hand, if YES, it is next determined whether or not the recalculation is performed with the range of the shift amount n of ± 5 as the recalculation range with respect to the shift amount Tminin2 of the provisional second minimum value Tfmin2 (step S152). ). If NO is determined, the process proceeds to step S168 described later. On the other hand, when it determines with YES, it is determined whether it is (shift amount nmin1> = shift amount Tnmin1) (step S154). If YES, the value DIS1 indicating the magnitude of the shift amount difference between the provisional minimum minimum value Tfmin1 and the recalculated minimum minimum value fmin1 is set to (DIS1 = nmin1-Tnmin1) (step S156), and if NO, the shift amount The difference DIS1 is set as (DIS1 = Tnmin1-nmin1) (step S158).

  Next, the CPU 60 determines whether or not (shift amount nmin1 ≧ shift amount Tnmin2) (step S160). If YES, the value DIS2 indicating the magnitude of the shift amount difference between the provisional second minimum value Tfmin2 and the recalculated minimum minimum value fmin1 is set to (DIS1 = nmin1−Tminin2) (step S162). The quantity difference DIS2 is set to (DIS2 = Tnmin2-nmin1) (step S164).

  Next, it is determined whether (DIS1 ≦ DIS2) (step S166), YES, that is, it is determined that the recalculation minimum minimum value fmin1 is closer to the temporary minimum minimum value Tfmin1 than the temporary second minimum value Tfmin2. In such a case, the provisional value ZANTEI = shift amount Tnmin1 is set (step S168). If NO, that is, if it is determined that the recalculated minimum minimum value fmin1 is closer to the provisional second minimum value Tfmin2 than the provisional minimum minimum value Tfmin1, the provisional value ZANTEI = shift amount Tnmin2 is set (step S170). If NO is determined in step S150 or step S152, the process proceeds to step S168, where the provisional value ZANTEI = shift amount Tnmin1.

  Next, the CPU 60 determines whether or not (ZANTEI ≧ nmin1), that is, whether the shift amount nmin1 of the recalculation minimum minimum value fmin1 is on the + side or the − side with respect to the provisional value ZANTEI (step S172). ). If YES is determined, the shift amount difference nSA is set to (nSA = ZANTEI-nmin1) (step S174).

  First, it is determined whether (nSA ≧ 3) (step S176). If YES is determined, ranging is impossible (step S178), and the shortage recalculation process is terminated.

  If NO is determined in step S176, it is subsequently determined whether (nSA = 2) or not (step S180). If YES is determined here, the correlation value f (nmin1-4) and the correlation value f (nmin1-5) at the shift amounts n = nmin1-4 and nmin1-5 are recalculated by normal calculation (step S182). ).

  If it is determined as NO in step S180, it is next determined whether or not (nSA = 1) (step S184). When it determines with YES, the correlation value f (nmin1-5) in shift amount nmin1-5 is recalculated by a normal calculation (step S186).

  When it is determined NO in step S184, recalculation (recalculation for shortage) is not performed (step S188), and the deficiency recalculation process is terminated.

  If NO is determined in step S172, the shift amount difference nSA = nmin1-ZANTEI is set (step S190).

  First, it is determined whether or not (nSA ≧ 3) (step S192). If YES is determined, ranging is impossible (step S194), and the shortage recalculation process is terminated.

  If NO is determined in step S192, it is subsequently determined whether (nSA = 2) or not (step S196). If YES is determined here, the correlation value f (nmin1-4) and the correlation value f (nmin1-5) at the shift amounts n = nmin1-4 and nmin1-5 and the correlation value f (nmin1-5) are recalculated by normal calculation (step S198). ).

  If NO is determined in step S196, it is next determined whether or not (nSA = 1) (step S200). When it determines with YES, the correlation value f (nmin1-5) in shift amount n = nmin1-5 is recalculated by a normal calculation (step S202).

  If NO is determined in step S196, recalculation (recalculation for shortage) is not performed (step S188), and the deficiency recalculation process is terminated.

  Note that if the shortage recalculation range is less than the minimum value (−2) of n or exceeds the maximum value (38) of n, the recalculation is not performed.

  Through the above shortage recalculation process, the correlation value f (n) by the normal calculation of the shift amount range necessary for the shift amount nmin of the maximum correlation value fmin is obtained.

  Next, a description will be given of erroneous ranging and its prevention, which are peculiar to every third i3 calculation. As described in the above “AF data acquisition process” section, an erroneous distance measurement specific to every third i calculation is performed when the sensor data subjected to contrast extraction processing (two-pixel difference calculation) is used as AF data. Especially problematic. FIGS. 50A and 50B are diagrams illustrating AF data used in every third i-th calculation at the shift amount n = 0, among the AF data of the R sensor 94 and the L sensor 96 obtained by the two-pixel difference calculation. is there. In this case, since every third correlation value calculation uses data, a portion having contrast cannot be captured. On the other hand, since the shift amount before and after this can capture a portion having contrast, there is a high possibility that the shift amount n = 0 will be a minimum value. When the shift amount n is deviated by 8 from this state (n = 8), the sensor number of AF data used in every third i3 calculation is shifted by 4, so that n = 0 except for one of the ends. The same AF data as in the case of is used. At this time, if there is no contrast in the newly added AF data at the end, there is a high possibility that the shift value n = 8 again becomes the minimum value.

  Such a phenomenon is repeated every time the shift amount is shifted by 8. FIG. 51 shows the result of calculating the correlation value f (n) by performing every i3 calculation in the AF data examples of FIGS. 50 (A) and 50 (B). As shown in FIG. Minimal values are detected in the quantities n = 0, 8, 16,. FIG. 52 shows the correlation value distribution when normal calculation is performed on the AF data examples of FIGS. 50 (A) and 50 (B). As described above, since the local minimum value is detected at a position unrelated to the true local minimum value detected in the normal calculation, there is a risk of erroneous distance measurement.

  FIG. 53 shows the correlation value f (n) obtained by calculation every third i3 when the sensor data mode as shown in FIG. 50 is obtained by actual measurement. In this case, the provisional minimum value Tfmin1 is detected with the shift amount Tminin1 = 33, and the provisional second minimum value Tfmin2 is detected with the shift amount Tminin2 = 26. In such an actual measurement, as shown in FIGS. 50A and 50B, there is almost no absence of contrast in all sensor data used when calculating the correlation value of a certain shift amount n, and there is a slight contrast. As a result, there is a case where the minimum value is not repeated at every shift amount interval of 8 due to the influence, and is observed at a shift amount interval of 7 or 9 with a deviation of ± 1.

Thus, the difference between the provisional minimum minimum value Tfmin1 and the provisional second minimum value Tfmin2 is smaller than the predetermined adjustment value a, and the shift amount between the provisional minimum minimum value Tmin1 and the provisional second minimum value. When the difference from Tnmin2 is a multiple of 8 or ± 1 of a multiple of 8, the correlation value calculation is performed by the above normal calculation instead of every i3 calculation. That is,
[Equation 13]
Tfmin2−Tfmin1 <Adjustment value a
And,
[Formula 14]
| Tnmin1-Tnmin2 | = (multiple of 8) or (± 1 of multiples of 8)
In case of, switch to normal calculation. By switching to the normal calculation, it is possible to prevent erroneous ranging unique to every third i-th calculation. When the correlation value calculation is performed by normal calculation instead of every i3 calculation, the difference between the shift amount Tminin1 of the provisional minimum minimum value and the shift amount Tnmin2 of the provisional second minimum value is a multiple of 8, or One condition is that it is ± 1 that is a multiple of 8. However, this condition is not limited to every other i3 operation, and this condition is generalized to ix with every x cell positions i for any value x. In the case of every other calculation, the difference between the shift amount Tminin1 of the provisional minimum minimum value and the shift amount Tminin2 of the provisional second minimum value is a multiple of (x + 1) × 2, or (x + 1) × 2 The condition is that the multiple is ± 1.

  Next, a second embodiment for reducing the distance measurement time in the correlation value calculation process will be described. In any case of the above-described normal calculation and every third i calculation, the correlation value f () for all shift amounts n (n = −2, −1, 0, 1,..., MAX (= 38)) in the employed sensor. In this second embodiment, the correlation value f (n) for all the shift amounts n is not calculated, but the correlation value f for the shift amounts n at regular intervals is calculated in the second embodiment. (n) is calculated. For example, every third shift amount n is calculated. The correlation value calculation in the case where every third shift amount n is taken is hereinafter referred to as “every three n3 calculation”. On the other hand, the correlation value in the case where the correlation value f (n) is obtained for all the shift amounts n. The value calculation is referred to as “normal calculation” in the description of the second embodiment (different from the meaning of “normal calculation” in the first embodiment). Further, the shift amount n for calculating the correlation value f (n) in every third n-th operation is referred to as adopted shift amount n.

  FIG. 54 (A) shows an example of the employed shift amount n in the calculation every third n3 when the number of employed sensors (that is, the number of employed sensors) is 62 and the window size is 42. Are set to every third n = 2, 6, 10, 14,..., 38 from the shift amount n = −2. 12, N1 indicates the shift amount of the L window 96B at each adopted shift amount n = −2, 2, 6,... With respect to the right end of the adopted sensor 96A, and N2 represents the adopted sensor 94A. The shift amount of the R window 94B at each adopted shift amount with respect to the left end of FIG.

  However, in the calculation every third n3, in addition to the adopted shift amount shown in FIG. 54A, the shift value n = −1, 0, 36, 37 shown in FIG. ) Is desirable. FIG. 55, FIG. 56, and FIG. 57 show the case where the minimum minimum value fmin1 is obtained for each of the long distance, the medium distance, and the short distance when the correlation value f (n) is obtained for all the shift amounts n by the normal calculation. , And the correlation value distribution when the correlation value f (n) is calculated using only the employed shift amount n (= 2, 6, 10, 14,..., 38) shown in FIG. The correlation when the correlation value f (n) is calculated with the adopted shift amount n (= -1, 0, 36, 37) shown in FIG. 54 (B) together with the adopted shift amount n shown in 54 (A). It is the graph which showed value distribution. The correlation value distribution of only the adopted shift amount n in FIG. 54 (A) is shown by a distribution connecting the dots indicated by “□” in the figure, and the correlation of the adopted shift amount n in FIGS. 54 (A) and 54 (B). The correlation value distribution is indicated by a distribution connecting points indicated by “「 ”in the figure.

  First, in the case where the minimum minimum value fmin1 originally detected by the normal calculation exists in the shift amount at a long distance, the minimum minimum value fmin1 is different from the adopted shift amount n in FIG. Suppose that it is in 1. In this case, when the correlation value f (n) is calculated using only the adopted shift amount n in FIG. 54A, the correlation value distribution calculated by this is shown in the correlation value distribution connecting the “□” symbols in FIG. Therefore, there occurs a situation in which the minimum value is not detected in the vicinity of the shift amount n = −1. On the other hand, when the correlation value f (n) is calculated at the adopted shift amount n (= −1, 0) in FIG. 54B, as shown in the correlation value distribution connecting the “▲” symbols in FIG. The minimum minimum value is detected at the shift amount n = −1, and the problem that the minimum minimum value fmin1 that should be detected on the long distance side is not detected is solved.

  Similarly, in the case where the minimum minimum value fmin1 originally detected by the normal calculation exists in the shift amount in the short distance, the minimum minimum value fmin1 is different from the adopted shift amount n in FIG. 54A. Let n = −37. In this case, when the correlation value f (n) is calculated using only the adopted shift amount n in FIG. 54A, the correlation value distribution calculated by this is shown in the correlation value distribution connecting the “□” symbols in FIG. Therefore, there occurs a situation in which the minimum value is not detected in the vicinity of the shift amount n = −37. On the other hand, if the correlation value f (n) is calculated at the adopted shift amount n (= 36, 37) in FIG. 54B, the shift is as shown in the correlation value distribution connecting the “▲” symbols in FIG. The minimum minimum value is detected in the quantity n = −37, and the problem that the minimum minimum value fmin1 that should be detected on the short distance side is not detected is solved.

  On the other hand, in the case where the minimum minimum value fmin1 that is originally detected by the normal calculation exists at a medium distance, the above-described problems hardly occur. For example, as shown by the thin line in FIG. 56, it is assumed that the minimum minimum value fmin1 obtained by the normal calculation is at a shift amount n = 16 different from the adopted shift amount n in FIG. In such a case, assuming that the correlation value f (n) is calculated using only the adopted shift amount n in FIG. 54A, the shift amount n = 16 as shown in the correlation value distribution connecting the “□” symbols in FIG. For example, a minimum value is detected at a shift amount n = 18 in the vicinity of. In general, when the shift amount nmin1 of the minimum minimum value fmin1 exists at a medium distance, a correlation value distribution in which the correlation value decreases from both sides toward the shift amount n is shown. Even when the correlation value f (n) is calculated only by n, the minimum value is detected at least in the vicinity of the shift amount n of the minimum minimum value fmin1. If the existence of the minimum value is known, the accurate shift amount nmin1 of the minimum minimum value fmi1 detected by the normal calculation can be detected by the recalculation described later, which is sufficient for the processing of every third n3 calculation.

  From the above, every third n3 calculation calculates the correlation value f (n) not only for the adopted shift amount n shown in FIG. 54 (A) but also for the adopted shift amount n shown in FIG. 54 (B). Is preferred. Hereinafter, the correlation value f (n) between every third adopted shift amount n as shown in FIG. 54A and a particular adopted shift amount n on the far side and near side as shown in FIG. 54B. It is assumed that every third n3 is calculated. However, the correlation value calculation may be performed using only the adopted shift amount n in FIG. 54A, and in this case, a correlation value distribution having no minimum value as shown in FIGS. 55 and 57 is obtained. In such a case, the recalculation described later may be performed on the assumption that there is a minimum value at a long distance or a short distance.

  When the CPU 60 calculates the correlation value f (n) at the adopted shift amount n by every n3 computations as described above, the CPU 60 calculates the correlation value f (n) from the distribution of the correlation values f (n) obtained at the adopted shift amount n. ) Detects the shift amount n at which the minimum value is obtained. When the detected minimum value is one, the minimum value is set as the provisional minimum minimum value Tfmin1, and when the detected minimum value is plural, the minimum minimum value is set as the provisional minimum minimum value. The correlation value f (n) is recalculated again by normal calculation again in the vicinity of the shift amount of the minimum minimum value. Note that this recalculation process can use the same method as the recalculation in every third i-th operation described above, the recalculation range, the processing method when there are a plurality of detected minimum values, and the correlation of the shortage. Detailed description of value calculation and the like will be omitted. The definition of terms is the same as that used in the description of every third i operation.

  The recalculation range is, for example, a shift amount range of ± 5 with respect to the shift amount Tminin1 of the provisional minimum value Tfmin1, and in the example of FIG. The recalculation range is the shift amount n = 13 to 23 as shown in FIG. However, for the adopted shift amount n for which the correlation value f (n) has already been calculated in every third calculation, it is not necessary to calculate the correlation value f (n) again by recalculation, and the recalculation range as shown in FIG. For the adopted shift amounts n = 14, 18, and 22 included in, the correlation value f (n) is not calculated in the recalculation.

  When the CPU 60 calculates the recalculation range correlation value f (n) by the above recalculation, the CPU 60 detects the shift amount nmin1 of the minimum minimum value fmin1 based on the recalculation range correlation value f (n), and the shift. Let the amount nmin1 be the shift amount nmin of the highest correlation to be detected in the correlation value calculation.

  Here, the number of operations when every third i-th operation or every third n-th operation is adopted is shown. For example, in the case of the above-mentioned number of employed sensors 62 and window size 42, in every third i-th operation, The number is 41/4 = 10.25, and the total number of recalculations 11 is 21.25. In every n3 computations, the number of computations is 15, and the total number of recalculations is 8 for a total of 23. On the other hand, in the case of the normal calculation, since the number is 41, it can be seen that the calculation number is sufficiently reduced and the distance measurement time is shortened in every third calculation and every third calculation.

  In the i3 calculation in the first embodiment described above, if the shift amount nmin1 of the provisional minimum minimum value Tfmin1 does not match the shift amount nmin1 of the minimum minimum value fmin1 of the normal calculation, the difference between the shift amounts is If it is larger, the number of operations in the shortage correlation value calculation increases, so the effect of shortening the time is reduced. Further, in every third i calculation, the number of data is ¼ that of the normal calculation, so the accuracy is lowered and the temporary minimum value may not appear. Such a phenomenon is often seen when the contrast of AF data is low.

  Therefore, if the contrast of the AF data in the employed sensor is greater than a predetermined reference value, every other i3 calculation is performed, and if it is low, the normal calculation is performed. . In addition, when the contrast is low, every third n3 calculation in the second embodiment may be performed.

{Details of Contrast Detection Processing (Steps S14 and S18 in FIG. 7)}
Next, the contrast detection process 1 in step S14 and the contrast detection process 2 in step S18 will be described in detail. The contrast detection is a process of detecting the presence or absence of contrast in the sensor image (AF data) based on the maximum value and the minimum value of AF data obtained from cells (sensor data) within a predetermined range of the AF sensor 74. In the present embodiment, the difference between the maximum value and the minimum value of the AF data is used as the contrast evaluation value. If the contrast is equal to or higher than a predetermined reference value, it is determined that there is contrast, and if it is smaller than the reference value, there is no contrast. judge.

  In contrast detection processing 1 in step S14 of FIG. 7, contrast detection is performed for all divided cells constituting the ranging area, that is, all cells of the divided area, that is, all cells of the employed sensor, while contrast in step S16. In the detection process 2, in each divided area, the contrast detection is performed with the shift amount nmin1 of the minimum minimum value fmin1 detected by the correlation value calculation, that is, all cells in the window range in the shift amount nmin of the maximum correlation value fmin as the target range. Do.

  FIG. 59 is a flowchart showing an overall procedure of a series of contrast detection processing by contrast detection processing 1 and contrast detection processing 2. When the CPU 60 acquires the AF data by the AF data acquisition process in step S12 of FIG. 7, each of the R sensor 94 and the L sensor 96 constituting the distance measurement area is processed as one process of the contrast detection process 1 in step S14 of FIG. The process of contrast detection 1 is performed with the divided areas as individual target ranges (step S250). Now, paying attention to a corresponding divided area of the R sensor 94 and the L sensor 96, the divided area of the R sensor 94 and the divided area of the L sensor 96 are referred to as an adopted sensor of the R sensor 94 and an adopted sensor of the L sensor 96, respectively. And Then, if contrast detection 1 is performed for these employed sensors, the CPU 60 detects the maximum value and the minimum value among the AF data of all the cells of the employed sensor of the R sensor 94. The maximum value of the AF data detected at this time is RMAX, and the minimum value is RMIN. Similarly, the maximum value and the minimum value are detected from the AF data of all the cells of the adopted sensor of the L sensor 96. The maximum value of the AF data detected at this time is LMAX, and the minimum value is LMIN.

Next, the CPU 60 calculates the contrast in the adopted sensor of the R sensor 94 as follows:
[Equation 15]
RMAX-RMIN (17)
The contrast in the sensor employed by the L sensor 96 is calculated by the following equation:
[Equation 16]
LMAX-LMIN (18)
Ask for. Next, as one process of the contrast detection process 1 in step S14 of FIG. 7, the CPU 60 performs the contrast determination 1 based on the contrast detected by the contrast detection 1 in step S250 (step S252). That is, the contrast RMAX-RMIN of the above equation (17) in the adopted sensor of the R sensor 94 and the contrast LMAX-LMIN of the above equation (18) in the adopted sensor of the L sensor 96 are respectively set with respect to the predetermined reference value R4.
[Equation 17]
RMAX-RMIN <R4 (19)
LMAX-LMIN <R4 (20)
It is determined whether or not holds. If either one of the above formulas (19) and (20) holds, it is determined that there is no contrast, and distance measurement in those employed sensors (the divided area of interest) is disabled (step S254). If neither of the above formulas (19) and (20) hold, the contrast in those employed sensors is assumed to be present.

  Next, the CPU 60 performs the correlation value calculation process in step S16 of FIG. 7 for the divided areas determined to have contrast in step S252 (step S256).

Next, as one process of the contrast detection process 2 in step S18 of FIG. 7, the CPU 60 performs contrast detection with the R window 94B and the L window 96B in the maximum correlation shift amount nmin detected by the correlation value calculation as the target range. 2 is performed (step S258). Now, paying attention to a corresponding divided area of the R sensor 94 and the L sensor 96, the divided area of the R sensor 94 and the divided area of the L sensor 96 are referred to as an adopted sensor of the R sensor 94 and an adopted sensor of the L sensor 96, respectively. And Then, if contrast detection 2 is performed for these employed sensors, the CPU 60 determines the maximum AF data value within the range of the R window 94B when the highest correlation shift amount nmin is obtained in the employed sensor of the R sensor 94. Detect the minimum value. The maximum value detected at this time is RWMAX, and the minimum value is RWMIN. Similarly, the maximum value and the minimum value of the AF data are detected in the range of the L window 96B when the maximum correlation shift amount nmin is obtained in the sensor employed by the L sensor 96. The maximum value detected at this time is LWMAX, and the minimum value is LWMIN. The CPU 60 calculates the contrast in the R window 94B by the following formula:
[Equation 18]
RWMAX-RWMIN (21)
Ask for. Further, the contrast in the L window 96B is expressed by the following equation:
[Equation 19]
LWMAX-LWMIN (22)
Ask for.

Subsequently, as a process of the contrast detection process 2 in step S18 of FIG. 7, the CPU 60 performs the contrast determination 2 based on the contrast obtained by the above equations (21) and (22) (step S260). That is, the contrast RWMAX-RWMIN of the above equation (21) in the R window 94B and the contrast LWMAX-LWMIN of the above equation (22) in the L window 96B are respectively compared with the reference value R4.
[Equation 20]
RWMAX-RWMIN <R4 (23)
LWMAX-LWMIN <R4 (24)
It is determined whether or not holds. If either one of the above formulas (23) and (24) is satisfied, the distance measurement is disabled in those employed sensors (the divided area of interest) with no contrast (step S254). If neither of the above equations (23) and (24) hold, the contrast in those windows is assumed to be present. If there is a contrast, the process proceeds to the next process.

  A specific example in the case where it is determined that distance measurement is impossible by the above contrast detection processing 1 and contrast detection processing 2 will be described. For example, when focusing on the center area of the R sensor 94 and the L sensor 96 as the adopted sensor, the AF data of the entire cell range of the adopted sensor shows low contrast as shown in FIGS. 60 (A) and (B). Suppose. In this case, when the correlation value f (n) is calculated by the correlation value calculation, the shift amount nmin1 of the minimum minimum value fmin1 as shown in FIG. = 12. In such a case, as shown in FIGS. 6A and 6B, in the adoption sensor of the R sensor 94 and the adoption sensor of the L sensor 96, the window range at the shift amount nmin (the window range having the highest correlation value). ) Also shows low contrast, and if the shift amount n = 12 is the highest correlation shift amount nmin, there is a high possibility of erroneous ranging.

  Thus, when AF data shows low contrast in the entire cell range of the adopted sensor, the correlation value calculation process is not actually performed on the adopted sensor, and contrast determination 1 in the contrast detection process 1 (in FIG. 59). In step S252), it is determined that distance measurement is impossible. Accordingly, since the correlation value calculation is not performed for the employed sensor that clearly shows AF data that cannot be measured, the distance measurement time is shortened.

  On the other hand, as shown in FIGS. 61A and 61B, it is assumed that the AF data shows high contrast in the entire cell range of the adopted sensors of the R sensor 94 and the L sensor 96. In this case, if the correlation value f (n) is calculated by the correlation value calculation, the shift amount nmin of the maximum correlation value fmin is detected with the shift amount n = 12, as shown in FIG. However, in this case, as shown in FIGS. 6A and 6B, in the adoption sensor of the R sensor 94 and the adoption sensor of the L sensor 96, the AF data in the window range having the highest correlation value is low contrast. If the shift amount n = 12 is the highest correlation shift amount nmin, there is a high possibility of erroneous ranging.

  As described above, even if it is determined that there is contrast in the contrast detection process 1 with all cells of the employed sensor as the target range, AF is not used in the window range in the maximum correlation shift amount nmin detected by the correlation value calculation. When the data shows low contrast, it is determined that distance measurement is impossible in contrast determination 2 (step S260 in FIG. 59) in the contrast detection process 2. Therefore, even in the case where the distance can be measured in the contrast detection process 1, in the case shown in FIG. 61 where the possibility of erroneous distance measurement is high, it is determined that the distance cannot be properly measured by the contrast detection process 2.

  In the contrast detection process described above, the correlation value calculation process (step S256 in FIG. 59) is performed, and then the contrast detection process 2 is performed for the window range in the maximum correlation shift amount nmin (step S258 in the figure). 260) Instead of performing the contrast detection processing 2 after performing the correlation value calculation processing, the same processing as the above-described contrast detection processing 2 may be performed before performing the correlation value calculation processing. For example, it is assumed that the contrast detection process 1 determines that there is contrast for the AF data of all cells in a certain divided area. In this case, next, the presence / absence of contrast is detected for each window range with respect to the window range in all shift amounts n of the divided area. As a result, the correlation value calculation is not performed for the shift amount n of the window range determined to have no contrast, and the correlation value calculation is performed only for the shift amount n of the window range determined to have contrast to calculate the correlation value f (n). To do. Then, the shift amount nmin of the maximum correlation value fmin is detected in the range of the shift amount n for which the correlation value f (n) is calculated. In this case, the number of calculations in the correlation value calculation can be reduced, and the distance measurement time can be shortened. In this case, the contrast detection process may not be performed. If it is determined that there is no contrast in the window range for all shift amounts n, the correlation value calculation is never performed and it is determined that distance measurement is impossible.

  Furthermore, if there is a window with contrast even at one location, all correlation value calculations may be performed.

{Details of L and R channel difference correction processing (step S20 in FIG. 7)}
Next, the L and R channel difference correction processing in step S20 in FIG. 7 will be described. The L and R channel difference correction processing is processing for matching the signal amounts of the AF data acquired from the R sensor 94 of the AF sensor 74 and the AF data acquired from the L sensor 96 of the AF sensor 74. In this process, in the divided area determined to have contrast in the contrast determination 2 (see FIG. 59) of the contrast detection process 2, the vicinity of the range of the R window 94B and the L window 96B (± 5n) where the highest correlation value was obtained. Performed on AF data.

First, the procedure of the L and R channel difference correction processing in the CPU 60 will be described with reference to the flowchart of FIG. The process of contrast determination 2 shown in step S300 in the figure corresponds to the contrast determination 2 in step S260 of FIG. 59. In this determination process, distance measurement is impossible for the divided areas determined to have no contrast. For the divided areas determined to have contrast, the process proceeds to the following L and R channel difference correction processing. Accordingly, a description will be given below by focusing on a certain divided area determined to have contrast as an adopted sensor. First, the CPU 60 determines whether or not correction is necessary. The minimum value of the AF data is detected and compared in the range of the window 94B and the range of the L window 96B, respectively, and it is determined whether the absolute value of the difference (left and right minimum value difference ΔDMIN) is large, small, or too large. (Step S302). Before or after this determination (between step S300 and step S302 or between step S302 and step S304), it is determined whether or not the highest correlation value in the employed sensor is equal to or greater than a predetermined reference value. It may be determined that the main correction is necessary only when the highest correlation value is equal to or higher than the reference value, and the main correction may not be performed when the highest correlation value is smaller than the reference value. Here, assuming that the minimum value of AF data in the R window 94B where the highest correlation value is obtained is RWMIN, and the minimum value of AF data in the L window 96 is LWMIN, the left-right minimum value difference ΔDMIN is given by the following equation:
[Equation 21]
ΔDMIN = | LWMIN−RWMIN | (25)
Is calculated by And for the predetermined reference values R5, R6 (R5 <R6),
[Equation 22]
ΔDMIN <R5 (26)
If the following holds, it is determined that the left-right minimum value difference ΔDMIN is small,
[Equation 23]
R6 ≧ ΔDMIN ≧ R5 (27)
Is satisfied, it is determined that the left-right minimum value difference ΔDMIN is large.
[Equation 24]
ΔDMIN> R6 (28)
Is satisfied, it is determined that the left-right minimum value difference ΔDMIN is too large.

  If it is determined by the above determination processing that the left and right minimum value difference ΔDMIN is small, the process proceeds to the next process without performing the main correction, and the right and left minimum value difference ΔDMIN is determined to be large (suitable for correction). Shifts to the process of the next step S304 in order to perform the main correction. If it is determined that the left-right minimum value difference ΔDMIN is too large, distance measurement is disabled (step S306).

  If it is determined that the left-right minimum value difference ΔDMIN is large, the CPU 60 next corrects the AF data of the correction range in the adopted sensor of the R sensor 94 and the L sensor 96 (step S304). The correction of the AF data is performed, for example, by obtaining a difference amount between the minimum value of the AF data in the adoption sensor of the R sensor 94 and the minimum value of the AF data in the adoption sensor of the L sensor 96 and reducing one of the difference amounts. The signal amount of the AF data of the other sensor is adjusted with respect to the AF data of the sensor. Details of the AF data correction will be described later. The AF data correction range is a range of AF data necessary for calculating the correlation value f (n) of the predetermined shift amount range in the correlation value calculation based on the corrected AF data in the processing of the next step S308. is there.

  When the AF data is corrected, correlation value calculation (correlation value calculation after correction) is performed again using the corrected AF data to obtain a correlation value f (n) ′ (step S308). The correlation value calculation after the AF data correction may be performed for all the shift amounts n. However, in the present embodiment, the vicinity of the shift amount nmin at which the highest correlation is obtained in the correlation value calculation before the AF data correction. For example, it is performed only for a range of ± 5 with respect to the shift amount nmin.

Next, the CPU 60 performs the shift amount obtained by performing the correlation value f (n) ′ by the correlation value calculation after the AF data correction based on the correlation value f (n) ′ obtained by the correlation value calculation after the AF data correction. In this range, the minimum minimum value fmin ′ and the shift amount nmin ′ are detected. Then, based on the minimum minimum value fmin ′, it is determined whether the AF data of the R sensor 94 and the AF data of the L sensor 96 after correction are low, high, or too low (step S310). ). Specifically, for example, the minimum minimum value fmin ′ after AF data correction is expressed by the following equation with respect to a predetermined reference value R7:
[Equation 25]
fmin ′> reference value R7 (29)
When satisfy | filling, it determines with a coincidence degree being too low. On the other hand, when Equation (29) does not hold, the minimum minimum value fmin ′ after AF data correction and the maximum correlation value fmin before correction are expressed by the following equation:
[Equation 26]
fmin ≦ fmin ′ (30)
When satisfy | filling, it determines with a coincidence degree being low. Equation (30) does not hold, and
[Equation 27]
fmin> fmin ′ (31)
Is satisfied, it is determined that the degree of coincidence is high.

  If it is determined by this determination process that the degree of coincidence is too low, distance measurement is disabled for this adopted sensor (step S306). If it is determined that the degree of coincidence is low, the correlation value calculation result before AF data correction is used instead of the correlation value calculation result after AF data correction in the subsequent processing (step S312). On the other hand, if it is determined that the degree of coincidence is high, the result of correlation value calculation after AF data correction is adopted (step S314). When adopting the result of correlation value calculation after AF data correction, the terms correlation value f (n), maximum correlation value fmin, and shift amount nmin used in the description of the subsequent processing are AF data correction. The subsequent correlation value f (n) ′, the minimum minimum value fmin ′, and the shift amount nnmin ′ are shown.

  Here, an embodiment of the AF data correction in step S304 will be described. FIG. 63 is a flowchart showing a processing procedure when the correction of AF data is performed by correcting the signal amount difference between the AF data of the R sensor 94 and the AF data of the L sensor 96. First, the CPU 60 determines a sensor (correction sensor) that corrects AF data among the R sensor 94 and the L sensor 96 (step S330).

  Specifically, the minimum value RMIN of the AF data in the R window in which the highest correlation is obtained is compared with the minimum value LMIN in the L window in which the highest correlation is obtained, and if RMIN> LMIN, the R sensor 94 Is the correction sensor, and when RMIN <LMIN, the L sensor 96 is the correction sensor.

When the L sensor 96 is a correction sensor, R and L are AF data before correction of the R sensor 94 and the L sensor 96, and RH and LH are AF data after correction.
[Equation 28]
LH = L-Signal amount difference RH = R
(Step S332). Here, the signal amount difference is LMIN−RMIN.

On the other hand, when the R sensor 94 is a correction sensor,
[Equation 29]
LH = L
RH = R−obtained from the signal amount difference (step S334). Here, the signal amount difference is RMIN−LMIN.

  The effect of the above L and R channel difference correction processing will be described. FIG. 64 is a diagram showing the effect of the L and R channel difference correction processing shown in FIG. 62 as a new method in comparison with the conventional method. Compared with the conventional method, the new method is different in the content of the determination process whether or not to perform correction in step S302 of FIG. 62, and the first conventional method (this method is the conventional method ( 1)), the minimum value of the AF data in the range of the R window 94B and the L window 96B where the highest correlation value was obtained is not compared as in the new method, but the R sensor 94 is not used. A method for comparing the minimum value RMIN for all AF data in the sensor and the minimum value LMIN for all AF data in the sensor employed by the L sensor 96 and correcting the AF data when the minimum value difference is larger than a predetermined value. It shall be said.

  As an example of the second conventional method (this method is referred to as the conventional method (2)), the average value of the AF data in each of the employed sensors of the R sensor 94 and the L sensor 96 is obtained, and the difference is a predetermined value. There is a method of correcting the AF data when it is larger than the above. A method of correcting the AF data of one sensor so that the average values match is assumed.

  For example, it is assumed that AF data as shown in FIGS. 64A and 64B is obtained in the adopted sensor (for example, the central area) of the R sensor 94 and the L sensor 96. This example of AF data shows a case where correction is not originally required. As a result of performing the correlation value calculation on the AF data in the adopted sensor, the correlation value distribution before the AF data correction (without correction) shows a distribution connected by “·” symbols in FIG. To do. In the correlation value distribution without correction, the maximum correlation shift amount nmin is detected at the shift amount n = 10. The ranges of the R window 94B and the L window 96B at that time are shown in FIG. In (B), the AF data distribution is indicated by a thick line.

  In such a case, when it is determined whether or not the AF data is corrected by the conventional method (1), each of the R sensor 94 and the L sensor 96 is apparent from the comparison between FIGS. Since there is a difference in the minimum value of the AF data in the sensor adopted (difference indicated by “Conventional (1)” in the figure), AF data correction (correction of the above signal amount difference) is performed. When the correlation value calculation is performed based on the corrected AF data, the correlation value distribution indicates a distribution connected by the symbol “Δ” in FIG. In the correlation value distribution obtained after the AF data correction, since the minimum minimum value is large, it is determined that the degree of coincidence between the signal amounts of the AF data of the R sensor 94 and the AF data of the L sensor 96 is low, and it is determined that distance measurement is impossible. Result. Conventionally, since the correlation value calculation is not performed before the AF data correction as in the new method, when it is determined that the degree of coincidence is low, the correlation value calculation before the AF data correction is performed as in the new method. No action has been taken to adopt the result of. As described above, in the conventional method (1), there is a case in which the distance measurement becomes impossible due to the correction even though the AF data that should not be the distance measurement is obtained.

  Further, when it is determined whether or not to correct the AF data by the conventional method (2), the difference between the average values of the AF data in the employed sensors of the R sensor 94 and the L sensor 96 (see FIGS. In the comparison of B), the difference shown in “Conventional (2)” has occurred, so AF data correction (correction of the above signal amount difference) is performed. When the correlation value calculation is performed based on the correlation value distribution, the correlation value distribution indicates a correlation value distribution connected by “x” symbols in FIG. In the correlation value distribution obtained after this AF data correction, since the minimum minimum value is large as in the conventional method (1), the degree of coincidence between the signal amounts of the AF data of the R sensor 94 and the AF data of the L sensor 96 is low. As a result, it is determined that ranging is impossible. As described above, even in the conventional method (2), there is a case in which the distance measurement becomes impossible due to the correction even though the AF data that should not be the distance measurement is obtained.

  When it is determined whether or not to correct the AF data by the new method as compared with the conventional methods (1) and (2) as described above, the R window 94B and the L window 96B where the highest correlation value is obtained in the employed sensor. Since there is no difference in the minimum value of the AF data within the range, correction is not performed and it is not necessary to perform extra correlation value calculation. Therefore, the problems as in the conventional methods (1) and (2) do not occur. If correction is made, the correlation value distribution is a distribution connected by the “□” symbol in FIG. 5C, and it is determined that the degree of coincidence is low as in the conventional methods (1) and (2). The However, in this case as well, in the case of the new method, it is determined that the result of correlation value calculation before AF data correction is adopted instead of being impossible to measure distance, so that AF data before correction can be measured without correction. In such a case, even if it is not appropriate to correct the AF data, it is possible to avoid a problem that distance measurement is impossible.

  Next, a description will be given of a case where another correction unit is employed in the AF data correction in step S304 instead of the signal amount difference correction shown in the flowchart of FIG. The correction of AF data described here is not only a difference in signal amount of AF data in the sensors employed by the R sensor 94 and the L sensor 96 but also a correction of the contrast ratio. The correction is performed on the AF data of the sensor that does not exceed the dynamic range among the AF data of the R sensor 94 and the AF data of the L sensor 96. Hereinafter, the details of the correction process will be described.

  First, the contrast correction amount and the offset correction amount used in the AF data correction formula will be described. The offset correction amount corresponds to a correction amount for correcting the signal amount difference. Here, the contrast correction amount is DLVCOMPA, the offset correction amount is DLVCOMPB, the AF data maximum value and minimum value in the adopted sensor of the R sensor 94 are R1MAX and R1MIN, and the AF data maximum value and minimum in the adopted sensor of the L sensor 96, respectively. The values are L1MAX and L1MIN, respectively, and the minimum values of AF data in the R window 94B and L window 96B where the highest correlation is obtained are R2MIN and L2MIN, respectively.

The expressions for obtaining the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMPB differ depending on the magnitude relationship between the minimum value R2MIN of AF data in the R window 94B and the minimum value R2MIN of AF data in the R window 94B where the highest correlation is obtained. The following formula,
[Equation 30]
L2MIN ≦ R2MIN (32)
Is satisfied, the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMB are expressed by the following equation:
[Equation 31]
DLVCOMPA = (R1MAX−R1MIN) / (L1MAX−L1MIN) (33)
DLVCOMB = R1MIN − {(R1MAX−R1MIN) / (L1MAX−L1MIN)} × L1MIN (34)
It is calculated by.

On the other hand,
[Formula 32]
L2MIN> R2MIN (35)
Is satisfied, the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMB are expressed by the following equation:
[Equation 33]
DLVCOMPA = (L1MAX−L1MIN) / (R1MAX−R1MIN) (36)
DLVCOMB = L1MIN − {(L1MAX−L1MIN) / (R1MAX−R1MIN)} × R1MIN (37)
It is calculated by.

  Once the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMPB are obtained from the above equations, calculation processing for correcting the AF data of the correction range in the employed sensor based on the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMB will be described next. Here, the AF data before correction of the R sensor 94 and the L sensor 96 is R and L, and the AF data after correction is RH and LH.

The following formula,
[Formula 34]
L2MIN ≦ R2MIN (38)
Is satisfied, the corrected AF data RH and LH are obtained by using the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMB obtained by the above equations (33) and (34), and the following correction equation:
[Equation 35]
LH = DLVCOMPA × L + DLVCOMB (39)
RH = R (40)
Is calculated by

On the other hand,
[Equation 36]
L2MIN> R2MIN (41)
In the case of the corrected AF data RH and LH, the following correction equation is obtained using the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMB obtained by the above equations (36) and (37):
[Equation 37]
LH = L (42)
RH = DLVCOMPA × R + DLVCOMB (43)
Is calculated by

The above AF data correction processing procedure is shown in the flowchart of FIG. The CPU 60 calculates the minimum value R2MIN and L2MIN of the AF data in the R window 94B and the L window 96B in which the highest correlation is obtained by the correlation value calculation process in step S16 in FIG.
[Equation 38]
L2MIN ≦ R2MIN (32)
It is determined whether or not holds (step S350). If the determination is YES, then the contrast correction amount DLVCOMPA is set to the above equation (33), that is,
[Equation 39]
DLVCOMPA = (R1MAX−R1MIN) / (L1MAX−L1MIN) (33)
(Step S352). Further, the offset correction amount DLVCOMB is expressed by the above equation (34), that is,
[Equation 40]
DLVCOMB = R1MIN − {(R1MAX−R1MIN) / (L1MAX−L1MIN)} × L1MIN (34)
(Step S354).

Then, the corrected AF data RH and LH are expressed by the above equations (39) and (40), that is,
[Equation 41]
LH = DLVCOMPA × L + DLVCOMB (39)
RH = R (40)
(Step S356).

On the other hand, if it is determined NO in step S350, the contrast correction amount DLVCOMPA is expressed by the above equation (36), that is,
[Formula 42]
DLVCOMPA = (L1MAX−L1MIN) / (R1MAX−R1MIN) (36)
(Step S358). Further, the offset correction amount DLVCOMB is expressed by the above equation (37), that is,
[Equation 43]
DLVCOMB = L1MIN − {(L1MAX−L1MIN) / (R1MAX−R1MIN)} × R1MIN (37)
(Step S360).

Then, the corrected AF data LH and RH are expressed by the above equations (42) and (43), that is,
[Equation 44]
LH = L (42)
RH = DLVCOMPA × R + DLVCOMB (43)
(Step S362).

  Next, the effect of the AF data correction processing when performing the contrast correction and offset correction (signal amount difference correction) described here is compared with the AF data correction processing (see FIG. 63) in which only the signal amount difference correction is performed. explain. The former is called a new method, and the latter is called a conventional method (Example 1).

  First, the correction result in the case where one of the R sensor 94 and the L sensor 96 is bright (when sunlight or the like is radiated to one sensor in a large amount → when there is a signal amount difference and no contrast difference) will be exemplified. 66A and 66B show the distribution of AF data before correction (without correction) and the distribution of AF data after correction according to the conventional method in the sensors (center area in the drawing) of the R sensor 94 and L sensor 96. FIG. The correction is performed on the AF data of the R sensor 94.

  On the other hand, FIGS. 67A and 67B show the same AF data distribution (without correction) as that in FIG. 66 and the AF data distribution after correction by the new method. This is performed for AF data.

  FIG. 68 shows the correlation value distribution when the correlation value f (n) is calculated based on the AF data before correction in FIGS. 66 and 67 and the AF data after correction by the new method and the conventional method. ing.

  As can be seen from the correlation value distribution of FIG. 68, when there is no contrast difference between the AF data of the R sensor 94 and the AF data of the L sensor 96 before correction, the same AF data correction is performed for both the conventional method and the new method. The result shows that the degree of coincidence between the signal amounts of the AF data of the R sensor 94 and the AF data of the L sensor 96 is improved as compared with the case without correction. In FIG. 69, the subject is actually arranged at a distance from the nearest to infinity, and the setting distance (vertical axis) of the photographing lens set by autofocus is corrected with respect to the distance (horizontal axis). This is a case where AF data is not corrected (when correction is not performed), AF data is corrected by a new method, AF data is corrected by a conventional method, and a case of design values. As is clear from this figure, there is a deviation from the design value in the case of no correction, but in the case of the conventional method and the new method, the setting distance of the taking lens is almost the same as the design value. I understand. From the above, when there is no contrast difference between the R sensor 94 and the L sensor 96, an appropriate result can be obtained by either the conventional method or the new method of AF data correction processing.

  However, when one of the R sensor 94 and the L sensor 96 is dark (when one sensor is hidden with a finger etc. → when there is a signal amount difference and a contrast difference), the new method is conventional. More advantageous results are obtained compared to the method. Next, the correction result in this case will be exemplified. FIGS. 70A and 70B show the distribution of AF data before correction (without correction) and the distribution of AF data after correction according to the conventional method in the sensors (center area in the drawing) of the R sensor 94 and L sensor 96. FIG. The correction is performed on the AF data of the R sensor 94.

  On the other hand, FIGS. 71A and 71B show the same AF data distribution before correction (without correction) as that in FIG. 70 and AF data distribution after correction by the new method. This is performed for AF data.

  FIG. 72 shows the correlation value distribution when the correlation value f (n) is calculated based on the AF data before correction in FIGS. 70 and 71 and the AF data after correction by the new method and the conventional method. Yes.

  As can be seen from the correlation value distribution of FIG. 72, the AF data correction by the new method shows a smaller minimum minimum value than the conventional method, and the signal amounts of the AF data of the R sensor 94 and the AF data of the L sensor 96 are smaller. The degree of matching is better. In FIG. 73, the subject is actually arranged at a distance from the closest distance to infinity, and the set distance (vertical axis) of the photographing lens set by AF is corrected for the AF data with respect to the distance (horizontal axis). In the case of not performing (without correction), the AF data is corrected by the new method, the AF data is corrected by the conventional method, and the case of the design value is shown. As is clear from this figure, the setting distance of the photographic lens is different from the design value in the case of no correction and in the conventional method, but in the case of the new method, the setting distance of the photographic lens is designed. It can be seen that the values are almost the same. From the above, when there is a contrast difference between the R sensor 94 and the L sensor 96, the AF data correction processing by the new method can obtain a more appropriate result than the conventional method. Therefore, the new method that can obtain an appropriate result regardless of the presence or absence of the contrast difference is more preferable than the conventional method.

  The R1MAX and R1MIN, and the L1MAX and L1MIN when calculating the contrast correction amount DLVCOMPA and the offset correction amount DLVCOMPB are the maximum value and the minimum value of the AF data in the sensors employed by the R sensor 94 and the L sensor 96, respectively. Not limited to this, the maximum value and the minimum value of the AF data in the range in which the AF data of the R sensor 94 and the L sensor 96 are corrected may be used. Further, R2MAX, R2MIN, L2MAX, and L2MIN may be used instead of R1MAX, R1MIN, L1MAX, and L1MIN. Further, R2MIN and L2MIN in the above equations (32), (35), (38), and (41) are the minimum values of the AF data in the R window 94B and the L window 96B where the highest correlation is obtained. Instead of L2MIN, the minimum value of AF data in the range in which AF data is corrected may be used.

{Minimum value judgment processing}
Next, the minimum value determination process will be described. The correlation value calculation process in step S16 in FIG. 7 and the L and R channel difference correction processes in step S20 in FIG. 7 include a process for detecting the minimum value of the correlation value f (n) from the correlation value distribution. At this time, it is determined whether or not there is a minimum value by the process of determining the minimum value described here. Basically, the correlation value determined as the minimum value is smaller than any correlation value in the shift amount adjacent to both sides of the shift amount, and the minimum of the minimum values is the minimum minimum value. .

  By the way, when the subject is located at a closer distance than the closest possible distance, the minimum value of the correlation value does not exist. FIG. 74 is a diagram showing an example of the correlation value distribution when it is assumed in this case that the correlation value f (n) is obtained up to the shift amount closer to the nearer side than 38 which is the maximum value of the shift amount n. is there. In such a correlation value distribution, since there is no minimum value in the range of the shift amount n = −2 to 38, it is determined that distance measurement is impossible with this adopted sensor. This determination is appropriate.

  However, even when the subject is present at a shorter distance than the closest distance, there may actually be a local minimum value as shown in FIGS. 75 shows the case of high contrast, and FIG. 76 shows the case of low contrast.

  On the other hand, as the basic determination content of the minimum value determination, when the minimum value is larger than the predetermined value, it is determined that the minimum value is not the minimum value. There is no local minimum value, and it is determined that distance measurement is not possible.

  On the other hand, in the case of FIG. 76, since the minimum value exists with a value smaller than 1000, there is a problem that it is determined that the minimum value exists in the above determination content.

  Therefore, in this local minimum value determination, the following determination is performed to eliminate the above-mentioned problem. When the subject is within a range that can be measured, the minimum value of the correlation value f (n) is usually the minimum value. On the other hand, when the minimum value is detected closer to the minimum minimum value than the shift amount, it can be predicted that the subject is further closer than the closest distance. Therefore, if there is a correlation value smaller than the minimum minimum value closer to the shift side of the minimum minimum value (the closer the shift amount, the closer the shift side), the short distance warning or the distance measurement is disabled.

  On the other hand, if there is a minimum value on the far side from the shift amount of the minimum minimum value, it is determined that there is some kind of abnormality (the shift amount n = 0 becomes infinity, so there can be no more infinity than infinity). Because it is possible, distance measurement is impossible.

{Details of Interpolation Value Calculation Processing (Step S22 in FIG. 7)}
Next, the interpolation value calculation process in step S22 of FIG. 7 will be described. The interpolation value calculation process is a process for detecting the shift amount of the highest correlation with higher accuracy from the peripheral correlation value f (n) with respect to the shift amount nmin in which the highest correlation (maximum correlation value fmin) is obtained in each divided area. It is. In the following description, the shift amount of the highest correlation detected by the interpolation value calculation process is referred to as a true shift amount of the highest correlation, and the value is indicated by x.

The CPU 60 performs the following processing in this interpolation value calculation processing. As shown in FIG. 77, the correlation value f (nmin-1) of the shift amount nmin-1 of -1 with respect to the shift amount nmin for which the highest correlation (maximum correlation value fmin (f (nmin))) was obtained in the employed sensor. And the correlation value f (nmin + 1) of the shift amount nmin + 1 of +1 is
[Equation 45]
f (nmin-1)> f (nmin + 1) (44)
Suppose that the relationship is satisfied. In this case, the CPU 60 determines the straight line L1 passing through the correlation values f (nmin) and f (nmin−1) of the shift amount nmin and the shift amount nmin−1, and the correlation value f (nmin + 1) of the shift amount nmin + 1 and the shift amount nmin + 2. The intersection with the straight line L2 passing through f (nmin + 2) is obtained. The intersection is set as the true maximum correlation shift amount x.

On the other hand, as shown in FIG. 78, the correlation value f (nmin-1) of the shift amount nmin-1 of -1 and the correlation value f (nmin + 1) of the shift amount nmin + 1 of +1 with respect to the shift amount nmin with which the highest correlation was obtained. And the following formula:
[Equation 46]
f (nmin−1) ≦ f (nmin + 1) (45)
Suppose that the relationship is satisfied. In this case, the CPU 60 determines the straight line L1 passing through the correlation values f (nmin-1) and f (nmin-2) of the shift amount nmin-1 and the shift amount nmin-2, and the correlation value f of the shift amount nmin and the shift amount nmin + 1. The intersection point with the straight line L2 passing through (nmin) and f (nmin + 1) is obtained. The intersection is set as the true maximum correlation shift amount x.

  However, as shown in FIG. 79, the minimum value of the shift amount n (shift amount n = −2) is the minimum value of the correlation value f (n), or the maximum value of the shift amount n (shift amount) as shown in FIG. When n = 38) is the minimum value of the correlation value f (n), these minimum values are not minimal values, and therefore the minimum value (shift amount n = -2) or maximum value (shift) of the shift amount n There is no case where the interpolation value is calculated with the amount n = 38) as the maximum correlation shift amount nmin. In this case, distance measurement is impossible.

  As shown in FIGS. 81A and 81B, a shift amount of +1 (shift amount n = −1) is the shift amount having the highest correlation with respect to the minimum value of shift amount n (shift amount n = −2). In the case of nmin, when the relationship of the above equation (44) is established as shown in FIG. 81A (in the case of FIG. 77), the interpolation value calculation can be performed. However, when the relationship of the above equation (45) is satisfied as shown in FIG. 81B (in the case of FIG. 78), the straight line L1 is not determined, and thus the distance measurement is impossible without performing the interpolation value calculation.

  As shown in FIGS. 82A and 82B, when the maximum value −1 (shift amount n = 37) of the shift amount n is the highest correlation shift amount nmin, as shown in FIG. 82B. When the relationship of the above equation (45) is satisfied (in the case of FIG. 78), the interpolation value can be calculated. However, when the relationship of the above equation (44) is established as shown in FIG. 82A (in the case of FIG. 77), the straight line L2 is not determined, and thus the distance measurement is impossible without performing the interpolation value calculation.

  The above processing is a principle processing. In the following case, the true maximum correlation shift amount x is obtained by other processing. For example, when the correlation value f (n) of the shift amounts nmin−2, nmin−1, nmin + 1, and nmin + 2 in the range of ± 2 is compared with the shift amount nmin where the highest correlation is obtained, the shift amount nmin is It is assumed that the correlation value f (n) shows a close value only with the three consecutive shift amounts n. In this case, it is considered that the correlation values at the three central points are shifted.

  In this case, instead of the above-described interpolation value calculation process, the true maximum correlation shift amount x is detected by the interpolation value calculation process described below. The interpolation value calculation process described above is referred to as an interpolation value normal calculation process, and the interpolation value calculation process described below is referred to as an interpolation value-specific calculation process. Further, specific determination as to whether or not the correlation value f (n) is a close value will be described later.

  When the correlation value f (n) shows a close value only at three consecutive shift amounts n as described above, the correlation value at the center point among the three shift amounts n is essentially the highest correlation value fmin. It is thought that it was to be detected. Therefore, the CPU 60 correlates the minimum shift amount ns among the three shift amounts n and the correlation values f (ns) and f (ns−1) between the shift amount ns and the shift amount ns−1 of −1. And a straight line L2 passing through correlation values f (nl) and f (nl + 1) between the maximum shift amount nl of the three shift amounts n and the shift amount nl + 1 which is +1 with respect to the shift amount nl. Find the intersection of The intersection is set as the true maximum correlation shift amount x.

  For example, as shown in FIG. 83, the maximum correlation shift amount nmin is detected as 0, and the maximum correlation value f (0) and the correlation value f (1 at shift amounts 1 and 2 of +1 and +2 with respect to the shift amount nmin. ) And f (2) are close to each other. In this case, an intersection point between the straight line L1 passing through the correlation value of the shift amount n = −1 and 0 and the straight line L2 passing through the correlation value of the shift amount n = 2 and 3 is obtained, and the intersection point is set as the shift amount x. Note that the processing when the correlation value at the +1 and +2 shift amounts is close to the maximum correlation value with respect to the maximum correlation shift amount nmin as in this example is referred to as processing type 1.

  As shown in FIG. 84, the maximum correlation shift amount nmin is detected as 0, and the maximum correlation value f (0) and the correlation value f at -1 and +1 shift amounts −1 and 1 with respect to the shift amount nmin. Assume that (−1) and f (1) are close to each other. In this case, an intersection point between the straight line L1 passing through the correlation value of the shift amount n = −2 and −1 and the straight line L2 passing through the correlation value of the shift amount n = 1 and 2 is obtained, and the intersection point is set as the shift amount x. The processing when the correlation value at the shift amount of −1 and +1 with respect to the shift amount nmin of the highest correlation becomes a value close to the highest correlation value as in this example is referred to as processing type 2.

  In addition, as shown in FIG. 85, the maximum correlation shift amount nmin is detected as 0, and the maximum correlation value f (0) and the correlation between −2 and −1 shift amounts −2 and −1 with respect to the shift amount nmin. Assume that the values f (−2) and f (−1) are close to each other. In this case, an intersection point between the straight line L1 passing through the correlation value of the shift amount n = −3 and −2 and the straight line L2 passing through the correlation value of the shift amount n = 0 and 1 is obtained, and the intersection point is set as the shift amount x. Note that the processing when the correlation value becomes close to the maximum correlation value in the shift amounts of −2 and −1 with respect to the maximum correlation shift amount nmin as in this example is referred to as processing type 3.

FIG. 86 is a flowchart showing a procedure for discriminating the processing types 1 to 3 of the above-described normal interpolation value calculation and interpolation value-specific calculation in the interpolation value calculation process. First, the CPU 60 determines that the correlation value difference f (nmin + 1) −f (nmin) between the maximum correlation value f (nmin) and the correlation value f (nmin + 1) at the shift amount nmin + 1 that is +1 with respect to the maximum correlation shift amount nmin. For the reference value R7,
[Equation 47]
f (nmin + 1) −f (nmin) <R7
It is determined whether or not the condition is satisfied (step S400). The reference value R7 is an upper limit value that can determine that two correlation values are close to each other. If the determination is NO, then the correlation value difference between the highest correlation value f (nmin) and the correlation value f (nmin-1) at the shift amount nmin-1 of -1 with respect to the shift amount nmin. f (nmin−1) −f (nmin) is the following formula with respect to the reference value R7:
[Formula 48]
f (nmin-1) -f (nmin) <R7
It is determined whether or not the condition is satisfied (step S402). If NO is determined in this determination process, an interpolation value normal calculation process is performed (step S432), the true maximum correlation shift amount x is detected, and the interpolation value calculation process is terminated.

On the other hand, if YES is determined in step S402, it is then determined whether or not there is a correlation value f (nmin-3) at the shift amount nmin-3 of -3 with respect to the shift amount nmin of the highest correlation ( Step S404). When it is determined NO, an interpolation value normal calculation process is performed (step S432). On the other hand, if YES is determined, the correlation value difference f (nmin−2) between the correlation value f (nmin−1) at the shift amount nmin−1 and the correlation value f (nmin−2) at the shift amount nmin−2. ) −f (nmin−1) with respect to the reference value R7,
[Equation 49]
f (nmin-2) -f (nmin-1) <R7
It is determined whether or not the condition is satisfied (step S406). If it is determined NO in this determination process, an interpolation value normal calculation process is performed (step S432). On the other hand, if YES is determined, the correlation value difference f (nmin-3) between the correlation value f (nmin-2) at the shift amount nmin-2 and the correlation value f (nmin-3) at the shift amount nmin-3. −f (nmin−2) with respect to the reference value R7,
[Equation 50]
f (nmin-3) -f (nmin-2) <R7
It is determined whether or not the condition is satisfied (step S408). If YES is determined in this determination process, an interpolation value normal calculation process is performed (step S432). On the other hand, if NO is determined, the correlation value difference f (nmin + 1) −f (nmin−2) between the correlation value f (nmin + 1) at the shift amount nmin + 1 and the correlation value f (nmin−2) at the shift amount nmin−2. ) For the reference value R7,
[Formula 51]
f (nmin + 1) -f (nmin-2) <R7
It is determined whether or not the condition is satisfied (step S410). If it is determined NO in this determination process, a process type 3 calculation process for each interpolation value is performed (step S412). That is, as shown in FIG. 85, it is assumed that there is a minimum value (true maximum correlation value) in the vicinity of the correlation value f (nmin−1), and the correlation values f (nmin−3), f (nmin−2), A shift amount x is obtained based on f (nmin) and f (nmin + 1).

  On the other hand, if YES is determined in step S410, a processing type 2 calculation process for each interpolation value is performed (step S420). That is, as shown in FIG. 84, it is assumed that there is a minimum value (true maximum correlation value) near the correlation value f (nmin), and the correlation values f (nmin-2), f (nmin-1), f ( The shift amount x is obtained based on nmin + 1) and f (nmin + 2).

If YES is determined in step S400, the CPU 60 determines the maximum correlation value f (nmin) and the correlation value difference f (nmin−1) at the shift amount nmin−1 of −1 with respect to the shift amount nmin of the highest correlation. ) And the correlation value difference f (nmin−1) −f (nmin) with respect to the reference value R7,
[Formula 52]
f (nmin-1) -f (nmin) <R7
It is determined whether or not the condition is satisfied (step S414). If YES is determined, next, the correlation value difference f (nmin-2) between the correlation value f (nmin-1) at the shift amount nmin-1 and the correlation value f (nmin-2) at the shift amount nmin-2. −f (nmin−1) is the following equation with respect to the reference value R7:
[Formula 53]
f (nmin-2) -f (nmin-1) <R7
It is determined whether or not the condition is satisfied (step S416). If YES is determined in this determination process, an interpolation value normal calculation process is performed (step S432). On the other hand, if NO is determined, then the correlation value difference f (nmin + 2) −f (nmin + 1) between the correlation value f (nmin + 1) at the shift amount nmin + 1 and the correlation value f (nmin + 2) at the shift amount nmin + 2 is For the reference value R7,
[Formula 54]
f (nmin + 2) −f (nmin + 1) <R7
It is determined whether or not the condition is satisfied (step S418). If YES is determined in this determination process, an interpolation value normal calculation process is performed (step S432). On the other hand, when it determines with NO, the processing type 2 calculation process according to interpolation value is performed (step S420).

If it is determined as NO in step S414, the CPU 60 determines whether or not there is a correlation value f (nmin-3) at a shift amount nmin-3 of -3 with respect to the shift amount nmin of the highest correlation (step S414). S422). When it is determined NO, an interpolation value normal calculation process is performed (step S432). On the other hand, if YES is determined, the correlation value difference f (nmin + 2) −f (nmin + 1) between the correlation value f (nmin + 1) at the shift amount nmin + 1 and the correlation value f (nmin + 2) at the shift amount nmin + 2 is the reference value R7. Is given by
[Equation 55]
f (nmin + 2) −f (nmin + 1) <R7
It is determined whether or not the condition is satisfied (step S424). When it is determined NO, an interpolation value normal calculation process is performed (step S432). On the other hand, if YES is determined, the correlation value difference f (nmin + 3) −f (nmin + 2) between the correlation value f (nmin + 2) at the shift amount nmin + 2 and the correlation value f (nmin + 3) at the shift amount nmin + 3 is the reference value R7. Is given by
[Formula 56]
f (nmin + 3) −f (nmin + 2) <R7
It is determined whether or not the condition is satisfied (step S426). If YES is determined, an interpolation value normal calculation process is performed (step S432). On the other hand, if NO is determined, then the correlation value difference f (nmin-1) between the correlation value f (nmin-2) at the shift amount nmin-1 and the correlation value f (nmin + 2) at the shift amount nmin + 2. -F (nmin + 2) is the following equation with respect to the reference value R7:
[Equation 57]
f (nmin-1) -f (nmin + 2) <R7
It is determined whether or not the condition is satisfied (step S428). If it is determined as NO, processing type 2 calculation processing for each interpolation value is performed (step S420). On the other hand, when it determines with YES, the processing type 1 calculation process according to interpolation value is performed (step S430). That is, as shown in FIG. 83, it is assumed that there is a minimum value (true maximum correlation value) near the correlation value f (nmin + 1), and the correlation values f (nmin−1), f (nmin), f (nmin + 2). , F (nmin + 3) to obtain the shift amount x.

{Details of AF error processing (step S24 in FIG. 7)}
Next, the AF error process in step S24 of FIG. 7 will be described. In the AF error process, if it is determined that distance measurement is impossible in all the divided areas of the 3 areas set or 5 areas set in the distance measurement area setting process (see step S10 in FIG. 7), a preset subject distance is set. In this process, the photographic lens is set at a fixed focal point so that it is in focus. Note that the process of the CPU 60 for setting the photographing lens to a fixed focus is called a fixed focus process, and this fixed focus process will be described below.

  When it is determined that distance measurement is impossible in all the divided areas of the distance measurement area, the CPU 60 determines in advance according to the cause of determination that distance measurement is impossible, film sensitivity, and the like as shown in the flowcharts of FIGS. Set the photographic lens to a fixed focus.

First, the CPU 60 determines whether or not AF pre-emission has been performed when it is determined that distance measurement is impossible in all the divided areas of the distance measurement area (step S450). If NO is determined, it is determined whether the sensor sensitivity is set to high sensitivity or low sensitivity (step S452). If it is determined that the sensitivity has been set low, the photographic lens is set to a fixed focus 6 m (step S454).
On the other hand, if it is determined in step S452 that the sensor sensitivity has been set to high sensitivity, the process proceeds to the flowchart of FIG. 88, and it is determined whether the film sensitivity is less than ISO400 or more than ISO400 (step S464). If it is determined that it is less than ISO 400, the photographing lens is set to a fixed focal point 3 m (step S466). On the other hand, if it is determined that the ISO lens is 400 or higher, the photographing lens is set to a fixed focus 6m (step S468).

  If it is determined as YES in step S450, the CPU 60 next determines whether the ranging area is set to 5 areas or 3 areas (step S456). If it is determined that the five areas are set, it is determined whether or not the cause of determining that ranging is impossible in all the divided areas is insufficient signal amount (the subject is dark) (step S458). If YES is determined, the photographing lens is set at an infinite position (step S460). On the other hand, if NO is determined, the process proceeds to the flowchart of FIG. 88, and the photographing lens is set at a fixed focus corresponding to the film sensitivity (details are omitted).

  When it is determined in step S456 that 3 areas are set, it is determined whether or not the cause of determining that ranging is impossible in all the divided areas is insufficient signal amount in the same manner as in the case of 5 areas (step S462). If YES is determined, the photographing lens is set at an infinite position (step S460). On the other hand, if NO is determined, the process proceeds to the flowchart of FIG. 88, and the photographing lens is set at a fixed focus corresponding to the film sensitivity (details are omitted).

{Details of Area Selection Processing (Step S28 in FIG. 7)}
Next, the area selection process in step S28 of FIG. 7 will be described. The area selection process is a process for selecting which of the subject distances of the subject distances calculated for each of the divided areas of the ranging area is to be used for focusing the photographing lens. In principle, the closest subject distance calculated in the divided areas other than the divided areas determined to be unable to be measured is employed. The subject distance in each divided area is obtained by the distance calculation process in step S26 of FIG. 7 based on the true maximum correlation shift amount x obtained by the interpolation value calculation process in step S22 of FIG.

  On the other hand, as an exception, when only the subject distance of either the left area or the right area is very close compared to other divided areas, that subject distance is not adopted and other divided areas are not used. Use the closest subject distance.

  More specifically, reference values 1 and 2 are set in advance for dividing the subject distance into three sections of the very close distance, the short distance, and the medium distance and beyond. The reference value 1 is set to, for example, 50 cm (distance at which a short distance warning occurs), and the reference value 2 is set to, for example, 4 m. Assume that the subject distance is calculated in each of the center area, the middle left area, the left area, the middle right area, and the right area when the distance measurement area is set to five areas. Then, the subject distance of only one of the left area and the right area is an extremely close distance that is closer than the reference value 1, and other divided areas (the divided areas of the left area and the right area that are not subject to an extremely close distance) Is included) and the subject distance is longer than the reference distance 2 and beyond the intermediate distance. In this case, the subject distance of the left area or the right area that is the very close distance is not adopted, and the nearest subject distance of the other divided areas is adopted. If at least one of the divided areas other than the left area or the right area, which is extremely close, has a subject distance that is very close or close, as a rule, subject distances of all the divided areas The closest subject distance is adopted.

  As a specific example, the subject distance for each divided area is an extremely close distance in which only the subject distance in the left area is closer than the reference value 1 as shown in FIG. 89A, and in the other divided areas, the reference value 2 Suppose the distance is longer than the middle distance. In this case, the subject distance of the nearest central area among the subject distances obtained in the divided areas other than the left area is adopted.

On the other hand, as shown in FIG. 89 (B), the subject distance in the left area is an extremely close distance nearer than the reference value 1, the subject distance in the central area is farther than the reference value 1 and closer than the reference value 2, and otherwise. It is assumed that the subject distance in the divided area is longer than the middle distance, which is farther than the reference value 2. In this case, as a general rule, the subject distance in the left area that is the closest is adopted. As shown in FIG. 89C, the subject distance in the left area is farther than the reference value 1 and closer to the reference value 2. Suppose that the subject distance in the other divided areas is longer than the middle distance that is longer than the reference value 2. In this case as well, as a general rule, the subject distance of the left area that is the closest is adopted.

  The same applies to the right area. As shown in FIG. 89D, only the subject distance in the right area is an extremely close distance that is closer than the reference value 1, and in the other divided areas, the intermediate distance is longer than the reference value 2. Suppose that In this case, the subject distance in the nearest left area among the subject distances obtained in the divided areas other than the right area is adopted.

  On the other hand, as shown in FIG. 89 (E), the subject distance in the right area is a very close distance nearer than the reference value 1, the subject distance in the left area is farther than the reference value 1 and closer than the reference value 2, and otherwise. It is assumed that the subject distance in the divided area is longer than the middle distance, which is farther than the reference value 2. In this case, as a general rule, the subject distance of the right area that is the closest among the subject distances of all the divided areas is adopted.

  Also, as shown in FIG. 89 (F), the subject distance in the right area is longer than the reference value 1 and close to the reference value 2, and the subject distance in other divided areas is longer than the reference value 2. Suppose it is farther away. Also in this case, as a general rule, the subject distance of the right area that is the closest among the subject distances of all the divided areas is adopted.

  The above exceptional processing can be similarly applied to the case where the distance measurement area is set to three areas. That is, in the case of three areas, the subject distance is very close to one of the right middle area and left middle area at both ends of the center area, right middle area, and left middle area constituting the ranging area. When the subject distance is more than the middle distance in the other divided areas, the closest subject distance among the subject distances other than the very close distance may be adopted.

  In the above embodiment, the sensor data is output from the AF sensor 74, converted into AF data by the CPU 60, and the correlation value calculation process is performed. However, the present invention is not limited to this. After converting the data into AF data, the AF data is output, and the correlation value calculation process is performed by the CPU 60. In the AF sensor 74, the sensor data is converted into AF data, and the correlation value calculation process is performed. Thereafter, a form in which a distance signal is output to the CPU 60 may be employed.

  The above embodiment is an example of an external light passive type distance measuring device, but the present invention can also be applied to a TTL passive phase difference method and the like.

  Furthermore, the camera distance measuring device in the above embodiment can be applied not only to the camera but also to a distance measuring device used for other purposes.

FIG. 1 is a front perspective view of a camera to which the present invention is applied. FIG. 2 is a rear perspective view of a camera to which the present invention is applied. FIG. 3 is a block diagram showing a control unit of a camera to which the present invention is applied. FIG. 4 is a diagram showing a configuration of a passive AF sensor. FIG. 5 is a diagram illustrating a sensor image (AF data) when the distance from the AF sensor to the subject is short. FIG. 6 is a diagram illustrating a sensor image (AF data) when the distance from the AF sensor to the subject is long. FIG. 7 is a flowchart showing an outline of the AF ranging process procedure in the CPU. FIG. 8 is a diagram showing divided areas in the sensor area of the R sensor and the L sensor. FIG. 9 is a flowchart showing the procedure of the distance measurement area setting process. FIG. 10 is a diagram showing a distance measurement area of 3 area setting and 5 area setting. FIG. 11 is an explanatory diagram used to explain the effect when the integration process is performed separately for each distance measurement area. FIG. 12 is an explanatory diagram used for explaining the correlation value calculation. FIG. 13 is a diagram showing a mode of the peak selection region set in the AF data acquisition process. FIG. 14 is a flowchart showing the procedure of AF data acquisition processing. FIG. 15 is a flowchart showing a processing procedure of three-division gain high integration. FIG. 16 is a diagram illustrating signal lines between the CPU and the AF sensor. FIG. 17 is an operation timing chart showing operation timings related to signal transmission / reception between the CPU and the AF sensor. FIG. 18 is an explanatory diagram used for explaining the peak selection region setting data. FIG. 19 is an explanatory diagram used for explaining the peak selection region setting data. FIG. 20 is an explanatory diagram used for explaining the peak selection area number data. FIG. 21 is an explanatory diagram used for explaining the procedure for generating the peak selection region setting data. FIG. 22 is an explanatory diagram used for explaining the procedure for generating the peak selection region setting data. FIG. 23 is an explanatory diagram used for explaining the procedure for generating the peak selection region setting data. FIG. 24 is an explanatory diagram showing peak selection region setting data and peak selection region number data. FIG. 25 is a diagram showing an output form when the / AFEND signal is not normally output. FIG. 26 is a diagram illustrating the state of the / AFEND signal and the MDATA signal when the subject is bright and dark. FIG. 27 is an explanatory diagram used for explaining the AF data reading process. FIG. 28 is an explanatory diagram used for explaining the conventional method and the new method for the two-pixel difference data. FIG. 29 is a diagram illustrating two-pixel difference data (AF data) obtained by the conventional method and the new method. FIG. 30 is a flowchart showing a correlation value calculation processing procedure when AF data is generated when the correlation value calculation is executed (during execution). FIG. 31 is a flowchart showing a correlation value calculation processing procedure when AF data is generated and stored in the RAM in advance before the correlation value calculation is executed. FIG. 32 is a diagram illustrating a data flow when AF data is generated during execution (during execution) of the correlation value calculation. FIG. 33 is a diagram showing a data flow when AF data is generated in advance and stored in the RAM before execution of the correlation value calculation. FIG. 34 is a flowchart showing sensor data reading processing in the conventional method. FIG. 35 is a timing chart showing the AFCLK signal and the AFDATAP signal when reading sensor data in the conventional method. FIG. 36 is a flowchart showing sensor data reading processing in the new method (the present invention). FIG. 37 is a timing chart showing the AFCLK signal and the AFDATAP signal at the time of reading sensor data in the new method (the present invention). FIG. 38 shows a comparison of distance measurement times between the new method (the present invention) and the conventional method. FIG. 39 is an explanatory diagram used for explaining the local minimum value determination process. FIG. 40 is an explanatory diagram used for explaining the local minimum value determination process. FIG. 41 is a diagram showing cell positions i of adopted cells in the R window 94B and the L window 96B in every third i calculation. FIG. 42 is a diagram illustrating an example of the correlation value distribution calculated by the normal calculation. FIG. 43 is a diagram showing an example of a correlation value distribution calculated by every third i operation. FIG. 44 is a diagram showing a recalculation range in every third i3 calculation. FIG. 45 is a diagram showing an example of the correlation value distribution when the provisional minimum value detected by the calculation of every third i is within the short distance warning range. FIG. 46 is a diagram illustrating an example of a correlation value distribution when a plurality of minimum values are detected by normal calculation. FIG. 47 is a diagram showing a correlation value distribution obtained when every third i-th calculation is performed based on the same AF data as FIG. FIG. 48 is a diagram showing a result of recalculation on the correlation value distribution of FIG. FIG. 49 is a flowchart showing the processing procedure of the shortage recalculation processing. FIGS. 50A and 50B are diagrams exemplifying AF data used for every third i-th calculation when the shift amount is n = 0, among the AF data of the R sensor and the L sensor obtained by the two-pixel difference calculation. . FIG. 51 is a diagram showing the result of calculating the correlation value f (n) by performing every i3 calculation in the AF data examples of FIGS. 50 (A) and 50 (B). FIG. 52 is a diagram showing a result of calculating a correlation value f (n) by performing normal calculation on the AF data examples of FIGS. 50 (A) and 50 (B). FIG. 53 is a diagram showing correlation values f (n) obtained by calculation every third i3 when the sensor data mode as shown in FIG. 50 is obtained by actual measurement. FIG. 54 is an explanatory diagram showing an example of the employed shift amount n in every third n3 calculation. FIG. 55 is an explanatory diagram used for explaining how to adopt the employed shift amount n in every third n3 calculation. FIG. 56 is an explanatory diagram used for explaining how to adopt the adopted shift amount n in every third calculation. FIG. 57 is an explanatory diagram used for explaining how to adopt the employed shift amount n in every third n3 calculation. FIG. 58 is a diagram showing an example of a recalculation range in every third n3 computation shown in FIG. FIG. 59 is a flowchart showing an overall procedure of a series of contrast detection processing by contrast detection processing 1 and contrast detection processing 2. FIG. 60 is a diagram showing an example of AF data and correlation value distribution when it is determined that distance measurement is impossible by contrast detection processing. FIG. 61 is a diagram showing an example of AF data and correlation value distribution when it is determined that distance measurement is impossible by contrast detection processing. FIG. 62 is a flowchart showing a procedure of L and R channel difference correction processing in the CPU. FIG. 63 is a flowchart showing a processing procedure when AF data is corrected by correcting a signal amount difference between AF data of the R sensor and AF data of the L sensor. FIG. 64 is an explanatory diagram used for explaining the effect of the L and R channel difference correction processing. FIG. 65 is a flowchart showing the AF data correction processing procedure in which AF data correction in the L and R channel difference correction processing is performed by correcting the signal amount difference and the contrast ratio. FIG. 66 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 67 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 68 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 69 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 70 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 71 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 72 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 73 is an explanatory diagram used for explaining the effect of the AF data correction processing in FIG. FIG. 74 is an explanatory diagram used for explaining the process of determining a minimum value. FIG. 75 is an explanatory diagram used for explaining the process of determining a minimum value. FIG. 76 is an explanatory diagram used for explaining the process of determining a minimum value. FIG. 77 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 78 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 79 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 80 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 81 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 82 is an explanatory diagram used for explaining the interpolation value calculation processing. FIG. 83 is an explanatory diagram used for explaining the calculation processing for each interpolation value. FIG. 84 is an explanatory diagram used for explaining the calculation processing for each interpolation value. FIG. 85 is an explanatory diagram used for explaining the calculation processing for each interpolation value. FIG. 86 is a flowchart showing a procedure for discriminating between processing types 1 to 3 of the interpolation value normal calculation and the interpolation value-specific calculation in the interpolation value calculation process. FIG. 87 is a flowchart showing the procedure of fixed focus processing. FIG. 88 is a flowchart showing the procedure of fixed focus processing. FIG. 89 is an explanatory diagram used for explaining the area selection processing.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Camera, 34 ... Shutter button, 60 ... CPU, 72 ... Strobe device, 74 ... AF sensor, 94 ... R sensor, 96 ... L sensor, 99 ... Processing circuit

Claims (8)

AF data generating means for forming a pair of AF data for calculating a correlation value based on a signal obtained from each light receiving element by forming an image of light from a distance measuring object on a pair of line sensors including a plurality of light receiving elements When,
AF data acquisition means for acquiring a pair of AF data from a pair of adopted sensor ranges used for ranging among the pair of line sensors;
Within the pair of adopted sensor ranges, a pair of window ranges for acquiring a pair of AF data used for correlation value calculation is determined, and the highest correlation within the pair of adopted sensor ranges is obtained for the pair of window ranges. First correlation value calculation means for sequentially calculating a correlation value while shifting for the first correlation to calculate a correlation value using AF data at predetermined intervals among the AF data in the pair of window ranges. Value calculation means;
The pair of window ranges are within a predetermined range before and after the shift amount of the window range when the highest correlation value among the correlation values obtained by the first correlation value calculating means is obtained. Second correlation value calculation means for sequentially calculating correlation values while shifting, second correlation value calculation means for calculating correlation values using all AF data within the pair of window ranges;
Distance measurement object distance calculation for calculating the distance of the distance measurement object based on the shift amount of the window range when the highest correlation value among the correlation values obtained by the second correlation value calculation means is obtained. Means,
A distance measuring device comprising: When there are a plurality of extreme values showing a high correlation by the correlation value calculation in the adopted sensor, the first correlation value calculating means sets the extreme value having the highest correlation as the temporary first extreme value, and then The high extreme value is obtained as the provisional second extreme value,
When the difference between the provisional first extreme value and the provisional second extreme value is within a predetermined reference value, the second correlation value calculation means is configured to display the window when the provisional first extreme value is obtained. Correlation value calculation within a predetermined range before and after the shift amount of the range as a reference and within a predetermined range before and after the shift amount of the window range when the provisional second extreme value is obtained The distance measuring apparatus according to claim 1, wherein:   The shift amount of the window range when the highest correlation value is obtained by the first correlation value calculation means, and the shift amount of the window range when the highest correlation value is obtained by the second correlation value calculation means 2. The distance measuring apparatus according to claim 1, further comprising: a means for obtaining a difference between the two and a determining means for disabling distance measurement when the obtained difference is equal to or greater than a predetermined shift amount.   The first shift amount of the window range when the temporary first extreme value is obtained by the first correlation value calculating means and the second shift amount of the window range when the temporary second extreme value is obtained. And a means for obtaining a difference between the window range and the third shift amount when the highest correlation value is obtained by the second correlation value calculating means, and a smaller difference among the obtained differences is predetermined. 3. A distance measuring apparatus according to claim 2, further comprising: a determination unit that disables distance measurement when the shift amount is greater than or equal to. The shift amount of the window range when the highest correlation value is obtained by the first correlation value calculation means, and the shift amount of the window range when the highest correlation value is obtained by the second correlation value calculation means Has a means to determine the difference between
2. The distance measuring apparatus according to claim 1, wherein the second correlation value calculation means performs an additional correlation value calculation beyond the predetermined range by an amount of the obtained difference shift amount.   The distance measuring object distance calculating means performs interpolation value calculation based on the highest correlation value among the correlation values obtained by the second correlation value calculating means and the correlation values in the predetermined range before and after the correlation value. 6. The distance measuring device according to claim 1, wherein a shift amount for obtaining a true maximum correlation value is obtained, and a distance of the distance measuring object is calculated based on the shift amount.   Determining means for determining whether or not the shift amount of the window range when the highest correlation value is obtained by the first correlation value calculating means is within the short distance side distance measurement unnecessary range; And a means for stopping the correlation value calculation by the second correlation value calculation means when it is determined that it is within the range not requiring distance measurement, the highest correlation value obtained by the first correlation value calculation means is also obtained. The distance measuring apparatus according to claim 1, wherein the distance of the object to be measured is calculated.   The pair of adopted sensors are sensors for all distance measuring areas of the pair of line sensors, or sensors for each divided area obtained by dividing the whole distance measuring area of the pair of line sensors into a plurality of divided areas. 8. The distance measuring device according to any one of 7 above.

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