JP2009021862A - Imaging apparatus, and imaging control method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an optimal image by performing high-precision pet detection. <P>SOLUTION: When a user selects the type of a pet, a CPU reads features of the pet stored in a ROM and determines whether the appearance is fine or smooth (S201, S203). In the case of fine appearance, contour emphasis is performed and in the case of smooth appearance, noise reduction processing is performed (S202, S204). Next, a ratio in occupation of a high-luminance portion and a low-luminance portion is determined (S205-S207). In a case where it is decided that the high-luminance portion is much and the low-luminance portion is also much, gradation correction is performed to widen dynamic ranges of both highlight/dark sides (S208) and in a case where it is decided that the high-luminance portion is much and the low-luminance portion is a little, gradation correction is performed to widen the dynamic range of the highlight side (S209). In a case where it is decided that the high-luminance portion is a little and the low-luminance portion is much, gradation correction is performed to widen the dynamic range of the dark side (S210). <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an imaging apparatus and an imaging control method, and more particularly, to an imaging apparatus and an imaging control method for performing pet detection control in an image.

Conventionally, an imaging apparatus having a face detection function is known. This imaging apparatus detects a face candidate area by performing skin color detection or the like, and performs face detection by matching the detected face candidate area with face pattern data stored in a database. Patent Document 1 describes a technique for improving face detection accuracy by dividing an input image into small areas and detecting candidate areas between eyes and candidate areas between eyes from the luminance characteristics of each small area. Has been.
JP 2001-216515 A

  It is possible to perform pet detection by using an animal database instead of a face database in face detection. However, there are many types of pets such as dogs, cats, and birds, and there are many hairs, colors, etc. even in the same dog, so detection performance that satisfies all types of pets with only one pattern is provided. Can not do it. The present invention has been made in view of such circumstances, and an object thereof is to provide an imaging apparatus and an imaging control method capable of performing pet detection with higher accuracy and providing an optimum image.

  In order to achieve the object, the imaging apparatus according to claim 1, an imaging unit that images a subject and converts it into image data, an input unit that a user specifies a pet to be imaged, and an appearance of the pet for each pet. A database for storing the characteristics of the above, a reading means for reading out the appearance characteristics of the designated pet from the database, an image processing means for performing predetermined image processing on the image data according to the characteristics of the read appearance, And pet detection means for detecting the pet from the image data subjected to the image processing.

  Thereby, the detection performance with respect to a pet can be improved.

  The imaging device according to claim 1, wherein an extraction unit that extracts the appearance characteristics of the pet from the captured image data of the pet, and the extracted appearance characteristics of the pet are registered in the database. And a registration unit for performing the above-described registration.

  Thereby, the detection performance with respect to the pet which he keeps can be improved.

  According to a third aspect of the present invention, in the imaging apparatus according to the first or second aspect, the image processing means performs the edge enhancement process more strongly as the designated pet contains more high-frequency components.

  Thereby, the resolution can be increased and the detection accuracy of a pet having a high frequency component can be increased.

  The imaging apparatus according to any one of claims 1 to 3, wherein the image processing unit performs noise reduction processing more strongly as the designated pet contains more low frequency components. And

  As a result, it is possible to remove high-frequency components that are not supposed to exist and improve the detection accuracy of pets with many low-frequency components.

  The imaging apparatus according to any one of claims 1 to 4, wherein the image processing unit has a dynamic range on a high luminance side when the designated pet includes a lot of high luminance portions. An enlarged gradation conversion process is performed.

  As a result, pets with high brightness can be prevented from becoming white and detection accuracy can be increased.

  6. The imaging apparatus according to claim 1, wherein the image processing unit has a dynamic range on a low luminance side when the designated pet includes a lot of low luminance portions. An enlarged gradation conversion process is performed.

  Thereby, it is possible to prevent blackening of a pet having a low luminance and increase detection accuracy.

  The imaging apparatus according to any one of claims 1 to 6, wherein when the pet detection unit detects the pet, the image processing unit adds the image of the pet to the captured image data. It is characterized in that predetermined image processing is performed in accordance with appearance characteristics.

  Thereby, an image suitable for the characteristics of the pet can be obtained.

  The imaging apparatus according to claim 7, wherein the image processing unit strongly resists the main imaged image data when the pet includes a portion with a high luminance and a low luminance. A noise reduction process is performed.

  Thereby, it is possible to obtain an image from which a high-frequency component that should not exist is removed.

  9. The imaging device according to claim 7 or 8, wherein the image processing means includes the image data that is actually captured when the pet includes a small number of low-luminance portions and a large luminance portion. In addition, a gradation conversion process for expanding a dynamic range on the high luminance side is performed.

  As a result, it is possible to obtain an image in which pets with high luminance are prevented from white spots.

  In order to achieve the object, the imaging apparatus according to claim 10 calculates an exposure correction condition when imaging the pet from the imaging unit that images the subject and converts the image into image data. Calculating means for registering, registration means for registering the calculated exposure correction condition in a database, reading means for reading the exposure correction condition from the database, and exposure to the image data in accordance with the exposure correction condition read from the database An exposure correction means for performing correction and a pet detection means for detecting the pet from the image data subjected to the exposure correction are provided.

  Thereby, the detection performance with respect to a pet can be improved.

  In order to achieve the object, an imaging control method according to claim 11, an imaging step of imaging a subject and converting it into image data, an input step in which a user specifies a pet to be imaged, and the specified pet A reading step of reading out appearance features from the database, an image processing step of performing predetermined image processing on the image data in accordance with the read out features of the appearance, and detecting the pet from the image data subjected to the image processing And a pet detection step.

  Thereby, the detection performance with respect to a pet can be improved.

  In order to achieve the above object, the imaging control method according to claim 12 includes an imaging step of imaging a subject and converting it into image data, and exposure correction conditions when imaging the pet from the captured pet image data. A calculation step for calculating, a registration step for registering the calculated exposure correction condition in a database, a reading step for reading the exposure correction condition from the database, and the image data according to the exposure correction condition read from the database. An exposure correction process for performing exposure correction and a pet detection process for detecting the pet from the image data subjected to the exposure correction are provided.

  Thereby, the detection performance with respect to a pet can be improved.

  According to the present invention, by performing image processing based on pet feature information registered in the database, it is possible to perform highly accurate pet detection and to provide an optimal image and imaging control. A method can be provided.

  The best mode for carrying out the present invention will be described below with reference to the accompanying drawings.

<First Embodiment>
FIG. 1 is a block diagram showing an electrical configuration of a first embodiment of a digital camera to which the present invention is applied.

  As shown in the figure, the digital camera 10 of the present embodiment includes an optical unit 11, a motor driving unit 12, a CCD 13, a timing generator 14, an analog signal processing unit 15, an A / D converter 16, an image input controller 17, Bus 18, detection unit 19, digital signal processing unit 20, flash 21, charging unit 22, CPU 23, memory card 24, external memory interface 25, LCD interface 26, LCD 27, memory interface 28, RAM 29, ROM 30, compression processing unit 31, etc. It is configured with.

  Each unit operates under the control of the CPU 23, and the CPU 23 controls each unit of the digital camera 10 by executing a predetermined control program based on an input from an operation unit (not shown).

  In addition to the control program executed by the CPU 23, various data necessary for control are recorded in the ROM 30. The CPU 23 controls each unit of the digital camera 10 by reading the control program recorded in the ROM 30 into the RAM 29 and sequentially executing the program.

  The RAM 29 is used as a program execution processing area, a temporary storage area for image data, and various work areas.

  An operation unit (not shown) includes general camera operation means such as a shutter button, zoom lever, menu button, execution button, cancel button, mode switching dial, and power button, and outputs a signal corresponding to the operation to the CPU 23.

  The optical unit 11 includes a focus lens, a zoom lens, and a diaphragm, and these are controlled to move by a motor drive unit 12.

  The CCD 13 is arranged at the rear stage of the optical unit 11 and receives subject light transmitted through the optical unit 11. The subject light transmitted through the optical unit 11 is imaged on the light receiving surface of the CCD 13 and converted into an electric signal by each light receiving element.

  The CCD 13 outputs the charge accumulated in each pixel as a serial image signal line by line in synchronization with the vertical transfer clock and horizontal transfer clock supplied from the timing generator 14. The CPU 23 controls the timing generator 14 to control the driving of the CCD 13. Note that the charge accumulation time (exposure time) of each pixel is determined by an electronic shutter drive signal given from the timing generator 14.

  The output of the image signal is started when the digital camera 10 is set to the shooting mode. In other words, when the digital camera 10 is set to the photographing mode, output of an image signal is started in order to display a through image on the LCD 27. The output of the image signal for the through image is temporarily stopped when the instruction for the main photographing is given, and is started again when the main photographing is finished.

  The image signal output from the CCD 13 is an analog signal, and this analog image signal is taken into the analog signal processing unit 15.

  The analog signal processing unit 15 includes a correlated double sampling circuit (CDS) and an automatic gain control circuit (AGC). The CDS removes noise contained in the image signal, and the AGC amplifies the noise-removed image signal with a predetermined gain. The analog image signal that has undergone the required image processing by the analog signal processing unit 15 is taken into the A / D converter 16.

  The A / D converter 16 converts the captured analog image signal into a digital image signal having a gradation width of a predetermined bit. This image signal is so-called RAW data, and has a gradation value indicating the density of R, G, and B for each pixel.

  The image input controller 17 has a built-in line buffer having a predetermined capacity, and stores the image signal for one frame output from the A / D converter 16. The image signal for one frame accumulated in the image input controller 17 is stored in the RAM 29 via the bus 18.

  In addition to the CPU 23, ROM 30, RAM 29, and image input controller 17, the detection unit 19, digital signal processing unit 20, external memory interface 25, LCD interface 26, memory interface 28, compression processing unit 31, etc. are connected to the bus 18. These are configured to be able to transmit / receive information to / from each other via the bus 18.

  The image signal for one frame stored in the RAM 29 is taken into the digital signal processing unit 20 dot-sequentially (pixel order).

  The digital signal processing unit 20 performs predetermined image processing on the image signals of R, G, and B colors captured in a dot-sequential manner, and outputs an image signal (Y / C) composed of a luminance signal Y and color difference signals Cr and Cb. Signal).

  The detection unit 19 detects a living thing or a pet contained in the subject from the image signal.

  The CPU 23 divides the shooting area (one screen) into a plurality of areas, calculates an integrated value of image signals for each R, G, and B for each divided area, and calculates a luminance value Y in each divided area from this integrated value. To do. When the integrated values of the image signals for R, G, and B are r, g, and b, the luminance value Y is expressed as [Equation 1].

[Equation 1]
Luminance value Y = 0.299 × r + 0.587 × g + 0.114 × b
Here, since r, g, and b each take a value from 0 to 255, the luminance value Y also takes a value from 0 to 255.

  Next, a temporary exposure value (Ev value) α is obtained from the calculated luminance value Y in each divided region. In addition, the calculation method of temporary exposure value (alpha) is not specifically limited, It can calculate using a well-known method. For example, in the method of calculating the average of the luminance values of the entire screen and using this as a temporary exposure value (so-called average metering method) or the average metering method, weighting the area near the center of the screen Method (so-called center forward point metering method), a method of calculating the average of brightness values in a very narrow area of an image, and using this as a temporary exposure value (so-called spot metering method), dividing the screen into a plurality of regions, Various methods are used such as a method (so-called divided photometry method) in which pattern recognition processing is performed to predict the most important region, the average of luminance values in the region is obtained, and this is used as a temporary exposure value. be able to.

  In the digital camera of the present embodiment, it is assumed that the user can select these photometry methods, and a temporary exposure value is calculated under the selected photometry method.

  The CPU 23 performs exposure correction as necessary for the calculated temporary exposure value α, and performs exposure setting based on the finally obtained exposure value (final exposure value) β. That is, the aperture value and shutter speed are determined from the final exposure value β according to a predetermined program diagram.

  When the photographing light amount is insufficient or when the main subject is determined to be backlit, the CPU 23 causes the flash 21 to be emitted by the capacitor charged by the charging unit 22 to obtain photographing auxiliary light.

  In accordance with a compression command from the CPU 23, the compression processing unit 31 performs compression processing of a predetermined format (for example, JPEG) on an image signal (Y / C signal) composed of the input luminance signal Y and color difference signals Cr and Cb, Generate compressed image data. Further, in accordance with a decompression command from the CPU 23, the input compressed image data is subjected to decompression processing in a predetermined format to generate uncompressed image data.

  The LCD interface 26 controls display on the LCD 27 in accordance with a command from the CPU 23.

  The external memory interface 25 controls reading / writing of data with respect to the memory card 24 in accordance with a command from the CPU 23.

  Next, the operation in the pet photographing mode will be described. The user can set the digital camera 10 to the pet photographing mode using an operation unit (not shown). In this pet photographing mode, first, it is necessary to select the type of pet to be photographed. If the pet to be photographed is a cat, “cat” is selected, and if it is a frog, “frog” is selected. When the type of pet to be photographed is set, the CPU 23 processes the signal output from the CCD 13 so as to facilitate pet detection. FIG. 2 is a flowchart showing the image processing operation.

  First, it is determined whether or not the appearance of the selected pet type is fine (step S201). The CPU 23 reads the characteristics of the selected pet type from the characteristics of the various pets stored in advance in the ROM 30. FIG. 3A is a diagram showing the characteristics of a cat, and FIG. 3B is a diagram showing the characteristics of a frog. Based on the feature thus read out, it is determined whether or not it is fine.

  If it is determined that the type of the selected pet is fine, a process for emphasizing the high frequency component is performed (step S202). A contour is extracted using the contour enhancement filter shown in FIGS. 4A and 4B, gain is applied to the extracted contour component, and the resultant is combined with the original image. FIG. 5A shows an input image, and FIG. 5B shows an image subjected to this processing. By performing such processing, it is possible to increase the detection accuracy of pet detection when a pet with hair like cats is a subject. Note that the filter matrix of the edge enhancement filter is not limited to the matrix in FIG. 4 and may be determined as appropriate.

  If it is determined that the subject is not fine, it is determined whether or not the subject is smooth (step S203). This determination is also made based on the features read from the ROM 30. If it is determined that the subject is smooth, noise reduction processing is performed using a median filter (step S204). FIG. 6A shows an input image, and FIG. 6B shows an image subjected to noise reduction processing. By reducing noise in this way, for example, when a pet having no hair, such as a frog, is a subject, it is possible to eliminate roughness that should not be present and improve detection accuracy of pet detection.

  Next, it is determined whether there are many parts with high luminance (step S205) and whether there are many parts with low luminance (steps S206, S207). This determination is also made based on the features read from the ROM 30.

  If it is determined that there are many high-luminance parts and many low-luminance parts, gradation correction is performed to widen the dynamic range on both the highlight and dark sides (step S208). FIG. 7A shows a normal gamma curve and a gamma curve obtained by enlarging the dynamic range of both the highlight and dark sides. By performing gradation correction using such a gamma curve, gradation can be given to the high-luminance part and the low-luminance part. The detection accuracy of pet detection can be increased.

  If it is determined that there are many high-luminance portions and few low-luminance portions, the dynamic range on the highlight side is expanded and gradation correction is performed (step S209). FIG. 7B shows a normal gamma curve and a gamma curve obtained by enlarging the dynamic range on the highlight side. By performing gradation correction using such a gamma curve, it is possible to provide gradation even in areas that are saturated and white tones when using a normal gamma curve, and there are many areas with high luminance. The detection accuracy of pet detection when the pet is the subject can be increased.

  If it is determined that there are few high-luminance parts and many low-luminance parts, gradation correction is performed to widen the dark side dynamic range (step S210). FIG. 7C shows a normal gamma curve and a gamma curve in which the dynamic range on the dark side is expanded. By performing gradation conversion using such a gamma curve, it is possible to give gradation to the dark side that would be blackened when using a normal gamma curve, and pets with many parts with low brightness will be subject In this case, the detection accuracy of pet detection can be increased.

  When the image processing is completed, the detection unit 19 performs pet detection using the signal subjected to this processing. Since this signal has been subjected to processing suitable for detection according to the type of pet, the detection accuracy of the pet can be improved. When a pet is detected, AE / AF is performed on the detected pet, and the user can perform photographing suitable for the pet.

  In this way, by performing processing on the input signal according to the type of the selected pet and performing pet detection using the processed signal, the detection accuracy can be increased according to the characteristics of the target pet. .

<Second Embodiment>
A digital camera 10 according to the second embodiment will be described. FIG. 8 is a block diagram illustrating an internal configuration of the digital camera 10 according to the second embodiment. The block diagram shown in FIG. 1 is different from the block diagram shown in FIG. In the digital camera 10 of the second embodiment, the user registers his / her pet and performs image processing according to the registered pet characteristics, thereby increasing the detection accuracy of pet detection.

  First, it is necessary to register the appearance characteristics of his / her pet in the digital camera 10. The user can register the pet characteristics by setting the my pet registration mode using an operation unit (not shown) and photographing the pet to be registered. FIG. 9 is a flowchart showing the operation in the my pet registration mode.

  First, a pet to be registered is photographed (step S901). When shooting in the My Pet Registration mode, in order to grasp the features of the pet's appearance, the pet is shot at a field angle that fills the frame or protrudes from the frame. Alternatively, a frame may be displayed on the through image, and shooting may be performed so that the pet fits within the frame, and features may be extracted from the image within the frame. Note that the appearance characteristics of the pet can be captured in more detail by performing a plurality of shootings from various angles such as from the side, rear, and top, as well as from the front.

  Next, weighting of “fine (high frequency component)” is performed on the photographed image (step S902). This weighting is performed by applying a high-pass filter to the captured image and extracting a high-frequency component. FIG. 10 is a diagram illustrating the “fine” weighting. As shown in FIG. 10, when the high-frequency component is 80% or more of the imaging region, the weight is 5, when the high-frequency component is 70% to 80%, 4, when the high frequency component is 50% to 70%, and 3, 30% to 50%. 2 and 30% or less. If a plurality of images are taken in step S901, the average of each image may be calculated and weighted.

  Next, the photographed image is weighted with “smooth (low frequency component)” (step S903). This weighting is performed by applying a low-pass filter to the captured image and extracting a low-frequency component. FIG. 11 is a diagram illustrating the weighting of “smooth”. As shown in FIG. 11, when the low frequency component is 80% or more of the region, the weight is 5, when the low frequency component is 70% to 80%, 3, when the low frequency component is 50% to 70%, and 3, 30% to 50%. 2 and 30% or less.

  Next, the captured image is weighted with “high brightness” (step S904). FIG. 12 is a diagram illustrating determination of a high-intensity part of a pet. As shown in FIG. 12, the weight is 5 when the area where the luminance is saturated to 255 is 40% or more, 4 when the area is 20% to 40%, the luminance is 20% or less, and the luminance. Is 3 when the area of 200 or more is 40% or more, 2 when the area of saturation is 20% or less and the area of luminance of 200 or more is 20% to 40%, and when the above is not applicable Set to 1.

  Next, the photographed image is weighted with “low brightness” (step S905). FIG. 13 is a diagram showing the determination of the low luminance part of the pet. As shown in FIG. 13, the weight is 5 when the area collapsed to 0% is 40% or more, 4 when the area is 20% to 40%, and the area collapsed to 0 is 20% or less. When the area of 60 or less is 40% or more, the area collapsed to 0 is 20% or less, and when the area of luminance 60 or less is 20% to 40%, 2 and when the above does not apply 1

  Finally, each obtained weight information is recorded in the my pet information recording unit 32 (step S906). FIG. 14 is a diagram illustrating an example of my pet information recorded in the my pet information recording unit 32. This completes the pet registration process.

  When the registration of the my pet is completed, the user can set the my pet shooting mode using an operation unit (not shown) and perform the pet shooting. FIG. 15 is a flowchart showing image processing in the My Pet shooting mode.

  When the user has set the my pet photographing mode, if a plurality of my pets are registered, the user selects a my pet to be photographed from among the plurality of my pets. When the my pet is selected, the CPU 23 reads out the my pet information from the my pet information recording unit 32, and determines whether or not there are many portions with high brightness using the read out my pet information (step S1501). Here, when the weight of “high luminance” is 3 or more, it is determined that there are many portions with high luminance. If it is determined that there are many portions with high luminance, it is determined whether there are many portions with low luminance (step S1502). Again, when the weight of “low luminance” is 3 or more, it is determined that there are many portions with low luminance. If it is determined that there are many portions with low luminance, gradation correction is performed to widen the dynamic range on both the highlight and dark sides (step S1503). The gradation correction for expanding the dynamic range on both the highlight and dark sides is performed using the gamma curve shown in FIG. 7A, as in the first embodiment. If it is determined that there are few portions with low luminance, gradation correction is performed to widen the dynamic range on the highlight side (step S1504). Also in this case, the gamma curve shown in FIG. 7B is used as in the first embodiment.

  If it is determined that there are few high-luminance parts, it is determined whether there are many low-luminance parts (step S1505). Again, when the weight of “low luminance” is 3 or more, it is determined that there are many portions with low luminance. If it is determined that there are many portions with low luminance, gradation correction is performed to widen the dark side dynamic range (step S1506). Tone correction for expanding the dynamic range on the dark side is performed using a gamma curve shown in FIG. 7C, as in the first embodiment. If it is determined that there are few portions with low luminance, the process proceeds to step S1507 without performing gradation correction.

  Next, contour emphasis processing is performed according to the “fine” weight of the My Pet information (step S1507). FIG. 16 is a diagram illustrating a filter matrix of the edge enhancement filter. When the “fine” weight is 1, the filters shown in FIGS. 16A and 16B, and when the weight is 2 to 4, the filters shown in FIGS. 16C and 16D, When the weight is 5, the filter shown in FIG. 16E or FIG. 16F is selected, and contour enhancement is performed using the selected filter.

  When the contour emphasis process is completed, a noise reduction process is then performed according to the “smooth” weight (step S1508).

  Here, a Wiener filter is used as the noise reduction process. The Wiener filter has a noise table (amount of noise for each luminance) for luminance, and performs a process of reducing noise by the amount of noise. By varying the amount of the noise table according to the “smooth” weight of the My Pet information, it is possible to increase or decrease the noise reduction according to the weight. FIG. 17 is a diagram illustrating a noise table of the Wiener filter. As shown in FIG. 17, the noise reduction effect is increased as the smoothness weight increases.

  This completes the image processing. The detection unit 19 performs pet detection using the signal subjected to the image processing. Since this signal is subjected to processing suitable for detection according to the registered pet, the detection accuracy of the pet can be improved. When a pet is detected, AE / AF is performed on the detected pet, and the user can perform photographing suitable for the pet.

  In this manner, by performing processing on the input signal according to the registered pet and performing pet detection using the processed signal, the detection accuracy can be increased according to the characteristics of the target pet.

  Note that the weighting threshold value is not limited to the numerical value of the present embodiment, and may be determined as appropriate.

<Third Embodiment>
A digital camera 10 according to a third embodiment will be described. FIG. 18 is a block diagram illustrating an internal configuration of the digital camera 10 according to the third embodiment. 8 is different from the block diagram shown in FIG. 8 in that an AF / AE control unit 33 for my pet is provided. In the digital camera 10 according to the third embodiment, the user registers his / her pet and controls the AE according to the characteristics of the pet, thereby increasing the detection accuracy of pet detection.

  Although it is necessary to register in advance the features of the appearance of his / her pet as my pet information, the registration of my pet information is the same as in the second embodiment, and a description thereof will be omitted. After registering the My Pet information, the user sets the My Pet shooting mode using an operation unit (not shown), and performs pet shooting. FIG. 19 is a flowchart showing image processing in the My Pet shooting mode.

  First, the mipet information is read from the mipet information recording unit 32, and the exposure correction amount is read from the “high brightness” condition (step S1901). FIG. 20 is a diagram showing the weight of “high brightness” and the exposure correction amount. As shown in FIG. 20, the exposure correction amount is determined for each condition. Next, the exposure correction amount is read from the condition “low brightness” (step S1902). FIG. 21 is a diagram showing the weight of “low brightness” and the exposure correction amount. As in FIG. 20, the exposure correction amount is determined for each condition.

  Next, an exposure value used for photographing is calculated (step S1903). The exposure correction amount read in step S1901 and the exposure correction amount read in step S1902 are added to the appropriate exposure calculated from the normal AE to determine the exposure value used for photographing. Using the exposure value calculated as described above, the detection unit 19 performs pet detection.

  For example, when shooting a black dog outdoors during the day, the AE moves to lower the exposure because the surroundings are bright. As a result, the black dog becomes darker and is crushed, making it difficult to detect in that state. When the weight of “low brightness” in the My Pet information is strong, the exposure is increased regardless of the ambient light intensity, and the pet itself is made brighter to make it easier to recognize the pet.

  Note that the actual photographing may be performed using the exposure value calculated here. By performing the main photographing as described above, it is possible to obtain an image with a preferable exposure according to the pet.

<Fourth embodiment>
A digital camera 10 according to a fourth embodiment will be described. The digital camera 10 according to the fourth embodiment photographs his / her pet and performs image processing suitable for the pet on the photographed image. The block diagram of the internal configuration is the same as that of FIG.

  Similar to the second embodiment, it is necessary to register the appearance characteristics of his / her pet in the digital camera 10 in advance. Since the registration of my pet information is the same as in the second embodiment, the description thereof is omitted.

  After registering the My Pet information, the user sets the My Pet shooting mode using an operation unit (not shown) and performs the main shooting. When the digital camera 10 determines that the mypet is captured in the photographic data, the digital camera 10 performs image processing shown in the flowchart of FIG. 22 and records the processed image signal in the memory card 24 via the external memory interface 25. The determination as to whether or not a My Pet is shown in the shooting data may be performed by detecting a pet from the through image, or by setting the My Pet shooting mode and determining that the My Pet is shown.

  Here, the image processing will be described. FIG. 22 is a flowchart showing image processing in the My Pet shooting mode. In addition, the same code | symbol is attached | subjected to the part which is common in the flowchart shown in FIG. 15, and the detailed description is abbreviate | omitted.

  First, the my pet information is read from the my pet information recording unit 32, and it is determined whether there are many portions with high luminance (step S1501) and whether there are many portions with low luminance (steps S1502 and S1505). I do. As in the second embodiment, it is determined that there are many portions with high brightness when the weight of “high brightness” is 3 or more, and the portion with low brightness when the weight of “low brightness” is 3 or more. It is judged that there are many. If it is determined that there are many high-luminance parts and few low-luminance parts, gradation correction is performed by expanding the dynamic range on the highlight side (step S1504). The gradation correction for expanding the dynamic range is performed in the same manner as in the first embodiment. By performing tone correction in this way, for example, white stripes on a white pet can be prevented.

  On the other hand, if it is determined that there are few high-luminance parts and many low-luminance parts, the noise reduction processing weight is set to plus 1 (step S2201). In this way, by changing the noise reduction processing, it is possible to appropriately reduce noise, for example, for a black pet with noticeable noise.

  If it is determined that both the high luminance part and the low luminance part are both large and if both are low, the process proceeds to step S1507.

  Next, contour emphasis processing is performed according to the “fine” weight of the My Pet information (step S1507). Similarly to the second embodiment, when the “fine” weight is 1, the filter shown in FIG. 16A or 16B, and when the weight is 2 to 4, FIG. Alternatively, the filter shown in FIG. 16D and when the weight is 5, the filter shown in FIG. 16E or FIG. In this way, by selecting the filter according to the fineness and performing the contour emphasis process, it is possible to leave details for, for example, a pet whose hair is bushy.

  When the contour emphasis process is completed, a noise reduction process is then performed according to the “smooth” weight of the read mypet information (step S1508). If the noise reduction weight is incremented by 1 in step S2001, 1 is added to the "smooth" weight, and the noise reduction process is performed stronger. If the weight becomes 6, the weight is 5. In the noise reduction process, a Wiener filter is used as in the second embodiment. The noise reduction effect for each weight is as shown in FIG. In this way, by performing noise reduction according to the weight of “smooth”, it is possible to appropriately reduce noise, for example, for a pet that originally slipped.

  This completes the image processing. By recording an image subjected to image processing in this way on the memory card 24, an image suitable for his / her pet can be obtained.

  Color information may be acquired as my pet information, and image processing may be performed based on the color information. By using color information, for example, a pet with a characteristic pattern or color is processed to make it vivid so as to emphasize the color, or a color is connected to a pet that is monotonous and has a large area. This makes it possible to soften the tone correction in consideration of the above, or to apply an offset to a pet with many black portions in order to express black well.

FIG. 1 is a block diagram showing an electrical configuration of a first embodiment of a digital camera to which the present invention is applied. FIG. 2 is a flowchart showing an image processing operation in the pet photographing mode. FIG. 3A is a diagram showing the characteristics of a cat, and FIG. 3B is a diagram showing the characteristics of a frog. FIG. 4 is a diagram illustrating a filter matrix of the edge enhancement filter. FIG. 5A shows an input image, and FIG. 5B shows an image subjected to the edge enhancement filter process. FIG. 6A shows an input image, and FIG. 6B shows an image subjected to low-pass filter processing. FIG. 7 is a diagram showing a gamma curve used for processing. FIG. 8 is a block diagram illustrating an internal configuration of the digital camera 10 according to the second embodiment. FIG. 9 is a flowchart showing the operation in the my pet registration mode. FIG. 10 is a diagram showing the determination of the fineness of the pet. FIG. 11 is a diagram illustrating the determination of the smoothness of the pet. FIG. 12 is a diagram illustrating determination of a high-intensity part of a pet. FIG. 13 is a diagram showing the determination of the low luminance part of the pet. FIG. 14 is a diagram illustrating an example of data recorded in the my pet information recording unit 32. FIG. 15 is a flowchart illustrating image processing in the mypet shooting mode according to the second embodiment. FIG. 16 is a diagram illustrating a filter matrix of the edge enhancement filter. FIG. 17 is a diagram illustrating a noise table of the Wiener filter. FIG. 18 is a block diagram illustrating an internal configuration of the digital camera 10 according to the third embodiment. FIG. 19 is a flowchart illustrating image processing in the mypet shooting mode according to the third embodiment. FIG. 20 is a diagram illustrating the weight of “high luminance” of the My Pet information and the exposure correction amount. FIG. 21 is a diagram showing the weight of “low luminance” of the My Pet information and the exposure correction amount. FIG. 22 is a flowchart illustrating image processing in the mypet shooting mode according to the fourth embodiment.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 11 ... Optical unit, 12 ... Motor drive part, 13 ... CCD, 14 ... Timing generator, 15 ... Analog signal processing part, 16 ... A / D converter, 17 ... Image input controller, 18 ... BUS, 19 ... Detection part, DESCRIPTION OF SYMBOLS 20 ... Digital signal processing part, 21 ... Flash, 22 ... Charging part, 23 ... CPU, 24 ... Memory card, 25 ... External memory interface, 26 ... LCD interface, 27 ... LCD, 28 ... Memory interface, 29 ... RAM, 30 ... ROM, 31 ... compression processing part, 32 ... my pet information recording part, 33 ... my pet AF / AE control part

Claims (12)

Imaging means for imaging a subject and converting it into image data;
An input means for a user to specify a pet to be imaged;
A database for storing the pet's appearance characteristics for each pet;
Reading means for reading out the features of the appearance of the designated pet from the database;
Image processing means for performing predetermined image processing on the image data in accordance with the read appearance characteristics;
Pet detection means for detecting the pet from the image data subjected to the image processing;
An imaging apparatus comprising: Extraction means for extracting the appearance characteristics of the pet from the image data of the captured pet;
Registration means for registering the extracted pet appearance characteristics in the database;
The imaging apparatus according to claim 1, further comprising:   The imaging apparatus according to claim 1, wherein the image processing unit performs an edge enhancement process more strongly as the designated pet contains a higher frequency component.   4. The imaging apparatus according to claim 1, wherein the image processing unit performs noise reduction processing more intensely as the designated pet contains more low-frequency components. 5.   5. The image processing unit according to claim 1, wherein the image processing unit performs gradation conversion processing for expanding a dynamic range on a high luminance side when the designated pet includes a lot of high luminance portions. The imaging device described.   6. The image processing unit according to claim 1, wherein the image processing unit performs gradation conversion processing for expanding a dynamic range on a low luminance side when the designated pet includes a lot of low luminance portions. The imaging device described.   7. The image processing unit according to claim 1, wherein when the pet detection unit detects the pet, the image processing unit performs predetermined image processing on the image data that has been actually captured according to the appearance characteristics of the pet. The imaging device according to any one of the above.   8. The imaging according to claim 7, wherein the image processing unit strongly performs noise reduction processing on the main imaged image data when the pet has a small number of high-luminance portions and many low-luminance portions. apparatus.   The image processing means performs gradation conversion processing for enlarging a dynamic range on a high luminance side to the imaged image data when the pet includes a low luminance portion and a high luminance portion. The imaging device according to claim 7 or 8. Imaging means for imaging a subject and converting it into image data;
Calculating means for calculating exposure correction conditions when imaging the pet from the image data of the captured pet;
Registration means for registering the calculated exposure correction condition in a database;
Read means for reading exposure correction conditions from the database;
Exposure correction means for performing exposure correction on the image data in accordance with the exposure correction conditions read from the database;
Pet detection means for detecting the pet from the image data subjected to the exposure correction;
An imaging apparatus comprising: An imaging process for imaging a subject and converting it into image data;
An input process in which the user specifies the pet to be imaged;
A reading step of reading out the characteristics of the appearance of the designated pet from a database;
An image processing step of performing predetermined image processing on the image data in accordance with the read appearance characteristics;
A pet detection step of detecting the pet from the image data subjected to the image processing;
An imaging control method comprising: An imaging process for imaging a subject and converting it into image data;
A calculation step of calculating an exposure correction condition when imaging the pet from the image data of the captured pet;
A registration step of registering the calculated exposure correction condition in a database;
A reading step of reading exposure correction conditions from the database;
An exposure correction step of performing exposure correction on the image data in accordance with the exposure correction condition read from the database;
A pet detection step of detecting the pet from the image data subjected to the exposure correction;
An imaging control method comprising:

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