JP2014053035A - Image processing apparatus, image processing method, and image processing program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an apparatus for recognizing an object displayed in an image without imposing an excessive processing load.SOLUTION: A control unit 2 of an image processing apparatus S gives a coordinate value to pixel data composing an image, extracts luminance information of the pixel data corresponding to the coordinate value, calculates an eigenvalue having a value corresponding to a feature point as the image of the pixel data given the coordinate value on the basis of the extracted luminance information, and applies filtering processing to the coordinate value with the calculated eigenvalue equal to or smaller than a predetermined threshold using a predetermined directional filter.

Description

  The present invention relates to a technical field such as an apparatus and method for recognizing an object displayed in an image.

  Image processing technology for recognizing an object displayed in an image is used in various fields. For example, in a digital camera and a digital video camera, a camera shake correction technique is realized by such an image processing technique.

  For example, in a digital video camera equipped with a camera shake correction function, a shootable area is narrowed to a certain size, and an image is read into a buffer memory at the time of shooting. Then, the subject of the first photographed image is recognized, compared with the subject of the image photographed thereafter, and the amount of protrusion of the subject from the imageable area is calculated. The photographable area is automatically shifted and recorded so that the subject comes to the center of the photographable area.

  In the above image recognition technology, the process of recognizing the subject of the first photographed image and comparing it with the subject of the image photographed thereafter is generally called tracking, and such tracking uses the Lucas-Kanade method. This is realized by calculating the optical flow.

  The optical flow refers to a motion vector for each pixel constituting the image in two consecutive images. That is, the optical flow is represented by a velocity vector for each pixel representing the motion of the image, and the motion is detected by obtaining the velocity vector.

  Therefore, in order to realize the calculation of the optical flow, it is necessary to accurately and reliably extract the motion of the feature point of the image (for example, the pixel or the velocity vector).

  In Patent Document 1, in order to obtain the movement of the feature point in two consecutive images, the feature point is divided into a low-reliability region and a high-reliability region, and the optical flow of the low-reliability region is An invention is disclosed in which weighting according to the reliability is performed on the optical flows in the 4-neighborhood or 8-neighborhood region and complemented using the weighted result.

Japanese Patent Laid-Open No. 11-339021

  However, such calculation of reliability places an excessive processing burden on the CPU (processing device). In recent years, digital cameras or digital video cameras have become capable of recording long-time moving images as the capacity of recording media increases. Since such a moving image is composed of an enormous amount of images, in order to calculate the optical flow for the moving image, the weighting must be performed on all the images constituting the moving image. I must. Therefore, when the invention described in Patent Document 1 is applied, the processing burden on the CPU further increases.

  Accordingly, the present invention has been made in view of the above problems, and an example of the object thereof is to provide an apparatus and a method for recognizing an object displayed on an image without giving an excessive processing burden. .

  In order to solve the above problem, the image processing apparatus according to claim 1, wherein a coordinate value is assigned to pixel data constituting an image, and luminance information of the pixel data corresponding to the coordinate value is extracted. Means, a calculation means for calculating an eigenvalue having a value corresponding to a feature point as the image of the pixel data to which the coordinate value is assigned based on the extracted luminance information, and the calculated eigenvalue is Filtering processing means for applying a filtering process to the coordinate values that are equal to or less than a preset threshold value using a preset directional filter.

  The image processing method according to claim 2, wherein an extraction step of assigning a coordinate value to pixel data constituting the image and extracting luminance information of the pixel data corresponding to the coordinate value, and the extracted luminance Based on the information, a calculation step of calculating an eigenvalue having a value corresponding to the feature point as the image of the pixel data to which the coordinate value is given, and the calculated eigenvalue is equal to or less than a preset threshold value And a filtering process step of performing a filtering process on the coordinate value using a preset directional filter.

  The image processing program according to claim 3 provides a computer included in the image processing apparatus to give coordinate values to pixel data constituting an image and extract luminance information of the pixel data corresponding to the coordinate values. Extraction means, calculation means for calculating eigenvalues having values corresponding to feature points as the image of the pixel data to which the coordinate values are assigned based on the extracted luminance information, and the calculated eigenvalues It is made to function as a filtering processing means for performing a filtering process on the coordinate value having a value equal to or less than a preset threshold value using a preset direction filter.

  According to these inventions, an eigenvalue having a value corresponding to a feature point as an image of pixel data to which a coordinate value of pixel data constituting an image is given is calculated, and the eigenvalue is equal to or less than a preset threshold value. A filtering process is performed on the coordinate values using a preset direction filter.

  Therefore, in order to obtain the motion of the feature points in two consecutive images, it is not necessary to perform calculation processing such as individually weighting the regions with low reliability of the feature points. Image processing such as tracking can be performed without imposing a heavy processing load.

  As described above, according to the present invention, the image processing apparatus calculates the eigenvalue having a value corresponding to the feature point as the image of the pixel data to which the coordinate value of the pixel data constituting the image is given, and the eigenvalue In order to obtain the motion of feature points in two consecutive images, since the filtering process is performed using a preset direction filter on the coordinate value for which is equal to or less than a preset threshold value, the reliability of the feature points is obtained. It is not necessary to perform a calculation process such as individually weighting a low area. Accordingly, image processing such as tracking can be performed without imposing an excessive processing burden on the CPU.

It is a block diagram which shows the structure of the image processing apparatus concerning this embodiment. It is a flowchart which shows operation | movement of the control part 2 concerning this embodiment. It is a figure which shows the example of a display screen at the time of extracting the said feature point, without performing the differential filter concerning this embodiment. It is a figure which shows the example of a display screen at the time of extracting the said feature point after giving the differential filter concerning this embodiment. It is a figure which shows the example of a display screen at the time of extracting the said feature point, without performing the direction filter concerning this embodiment. It is a figure which shows the example of a display screen at the time of extracting the said feature point after giving the direction filter concerning this embodiment.

  First, the best embodiment of the present application will be described with reference to the accompanying drawings. The embodiment described below is an embodiment when the present application is applied to an image processing apparatus.

  First, the configuration and functional overview of the image processing apparatus according to the present embodiment will be described with reference to FIG.

  FIG. 1 is a block diagram illustrating a configuration of an image processing apparatus according to the present embodiment.

  The image processing apparatus S includes an optical system engine 1, an extraction unit, a calculation unit, a control unit 2 as a filtering processing unit, a display unit 3 that displays an image as output data, and the like.

  The optical system engine 1 includes a lens (not shown), an aperture mechanism, an image sensor (for example, a charge coupled semiconductor (CCD) image sensor or a complementary metal-oxide semiconductor (CMOS) image sensor), a color filter, an interface unit, and the like. .

  Specifically, in the optical system engine 1, the light taken in from the lens is adjusted by a diaphragm mechanism or the like and formed as an optical image on the image sensor. Such an optical image (including a still image and a moving image) is converted into an electrical signal by an image sensor and output to an interface unit.

  The optical system engine 1 further includes an optical low-pass filter (not shown) for limiting the spatial frequency input to the interface unit, and an infrared (not shown) for cutting long wavelength components other than the visible light region input to the interface unit. A cut filter or the like is provided.

  The interface unit is an interface for outputting a captured image to the control unit 2.

  Specifically, the interface unit outputs the captured image to the control unit 2 as input data including pixel data indicating pixels constituting the image and color data corresponding to the pixels. .

  The control unit 2 is a processing circuit for performing image processing based on the input data and generating output data indicating a planar image and a moving image that can be viewed two-dimensionally. And a ROM.

  The control unit 2 performs tracking as an example of such image processing in order to realize a camera shake correction function, for example.

  In an image processing apparatus equipped with a camera shake correction function, a shootable area is narrowed to a certain size, and an image is read into a buffer memory at the time of shooting. Then, the subject of the first photographed image is recognized, compared with the subject of the image photographed thereafter, and the amount of protrusion of the subject from the imageable area is calculated. The photographable area is automatically shifted and recorded so that the subject comes to the center of the photographable area.

  In the above image recognition technology, the process of recognizing the subject of the first photographed image and comparing it with the subject of the image photographed thereafter is generally called tracking, and such tracking uses the Lucas-Kanade method. This is realized by calculating the optical flow.

  Here, the tracking and the Lucas-Kanade method are well-known techniques, and detailed description thereof is omitted. However, the optical flow refers to a motion vector for each pixel constituting the image in two consecutive images. That is, the optical flow is represented by a velocity vector for each pixel representing the motion of the image, and the motion is detected by obtaining the velocity vector.

  Therefore, in order to calculate an accurate optical flow, the control unit 2 needs to accurately and reliably extract feature points of the image such as the pixel data.

  Next, operation | movement of the control part 2 concerning this embodiment is demonstrated using FIGS.

  As described above, the control unit 2 calculates and tracks an optical flow using the Lucas-Kanade method. And the control part 2 extracts the feature point of an image in calculating an optical flow.

  Here, in general, when an image shows a gentle luminance gradient (for example, the object shown in the image has a certain pigment with little change in luminance indicated by the object, such as a cloudy sky or a car dashboard) If it is, it is difficult for the control unit or the like of the image processing apparatus to extract the feature points.

  Therefore, when extracting the feature points, the control unit 2 assigns coordinate values to the pixel data constituting the image, and extracts luminance information of the pixel data corresponding to the coordinate values (for example, grayscale conversion). And eigenvalues as an example of the luminance gradient that is the magnitude of the luminance indicated by the luminance information are calculated, and a preset filter is used for the coordinate values for which the eigenvalue is less than or equal to a preset threshold value. Filtering processing.

  Below, the said operation | movement of the control part 2 is demonstrated in detail.

  FIG. 2 is a flowchart showing the operation of the control unit 2 according to the present embodiment.

  First, the control unit 2 assigns coordinate values to pixel data constituting an image that is an object of image processing.

  For example, it is assumed that the control unit 2 assigns a coordinate value of M rows × N rows as a coordinate value for pixel data constituting a certain image.

  Then, the control unit 2 performs gray scale conversion on the coordinate values so as to extract luminance information of the pixel data corresponding to the coordinate values (step S1).

  The gray scale conversion is a known technique, and thus detailed description thereof is omitted. However, the gray scale conversion converts arbitrary pixel data into a gradation display of only brightness (that is, luminance information that is the magnitude of luminance). The pixel data after conversion has luminance information.

  Next, the control unit 2 calculates an eigenvalue of the image (step S2).

  It is difficult to extract the feature point at a place where the luminance gradient in the image (the luminance difference between adjacent pixel data, also referred to as a density gradient) is gentle (small), and the eigenvalue The value of has the characteristic of becoming smaller. On the other hand, when the amount of change in the luminance gradient is large, the control unit 2 has a characteristic that the feature points can be extracted satisfactorily.

  Therefore, when the control unit 2 extracts the feature points, is it difficult for the control unit 2 to extract the feature points for the pixel data by calculating eigenvalues for the target pixel data? Whether or not can be determined in advance.

  Note that the point where it is difficult to extract the feature points can be conceptualized as having low reliability because the possibility that an accurate optical flow is calculated is low.

  Therefore, the control unit 2 calculates the eigenvalue as a luminance gradient in the image and determines in advance whether it is difficult to extract the feature point.

  Therefore, the control unit 2 determines whether or not the eigenvalue is less than or equal to a preset threshold value (step S3).

  Hereinafter, calculation of the eigenvalue G as the eigenvalue and determination of whether or not the eigenvalue is equal to or less than a preset threshold will be described.

  First, when the differential value of the image with respect to the image region ω is g (p), the eigenvalue G is expressed by Expression (1).

  Then, a case where a 2 × 2 coordinate value as shown in Expression (2) is given to the image is taken as an example.

  The calculation result of the differential value of the image is a value represented by Expression (3).

  Then, when the eigenvalue G is calculated based on the value of the formula (3), the value shown in the formula (4) is obtained.

  From equation (4), it can be seen that the value of the eigenvalue G is 0 and is low. Therefore, it can be understood that the control unit 2 has difficulty in extracting the feature points from the pixel data constituting the image to which the coordinate values represented by the equation (2) are given (the reliability is low).

  On the other hand, a case where a 2 × 2 coordinate value as shown in Expression (5) is given to the image is taken as an example.

  The calculation result of the differential value of the image is a value represented by Expression (6).

  Then, when the eigenvalue G is calculated based on the value of the equation (6), the value shown in the equation (7) is obtained.

  From equation (7), it can be seen that the value of the eigenvalue G is 2 and is high. Therefore, it can be seen that the control unit 2 can satisfactorily extract the feature points (high reliability) for the pixel data constituting the image to which the coordinate values shown in Expression (5) are given.

  In the above-described example, regarding the determination of the size of the eigenvalue G, the case where the value of the eigenvalue G is 0 is low and the case of 2 is high, but the present invention is not limited to this.

  That is, in order to determine the magnitude of the eigenvalue G, a threshold value is set in advance, and the determination is performed based on the threshold value. In other words, this threshold value can be taken as an arbitrary value.

  Returning to the description of FIG. 2, when the eigenvalue is equal to or less than a preset threshold value (step S3: YES), the control unit 2 performs a filtering process using a preset filter (step S4).

  Such filtering processing is processing performed to detect a uniform edge in a region where it is difficult to extract the feature points (low reliability). For example, a differential filter or a directional filter is applied. Is done.

  First, a differential filter as an example of filtering processing will be described below.

  As an example of the differential filter, if there is a filter W having U rows × V columns, for example, if all the pixel data (pixels) of the pixel data constituting the image is f (i, j), the pixel data In contrast, the expression (8) is applied, and the applied result is represented by g (i, j).

  Here, the result of extracting the feature points without applying the differential filter and the result of extracting the feature points after applying the differential filter will be described with reference to FIGS. 3 and 4.

  First, before comparing FIG. 3 and FIG. 4, each figure will be described.

  FIG. 3 is a diagram illustrating an example of a display screen when the feature points are extracted without performing the differential filter according to the present embodiment.

  FIG. 3A shows an image obtained by capturing the surface of the iron plate before extracting the feature points without applying the differential filter. Since the iron plate is monochromatic and has no pattern, it may show a gentle luminance gradient. I understand.

  FIG. 3B shows an image obtained by imaging the surface of the iron plate when the feature points are extracted without applying a differential filter. The white points shown in FIG. 3B are the extracted feature points.

  FIG. 4 is a diagram showing an example of a display screen when the feature points are extracted after performing the differential filter according to the present embodiment.

  FIG. 4A shows an image obtained by imaging the surface of the iron plate after being subjected to the differential filter. As a result of applying the differential filter to the iron plate, the brightness gradient of the iron plate is clearly shown. .

  FIG. 4B shows an image obtained by imaging the surface of the iron plate when the feature points are extracted after applying the differential filter. Similar to FIG. 3B, the white point shown in FIG. 4B is the extracted feature point.

  Here, comparing the number of white spots shown in FIGS. 3B and 4B, it can be seen that the number of white spots shown in FIG. 4B is larger. This indicates that, in images obtained by imaging the same object, more feature points can be extracted in the image subjected to the differential filter.

  Next, a direction filter (two-dimensional filtering process) will be described as another example of the filtering process.

  The directional filter is a process of applying the filter in the horizontal, vertical, and diagonal directions, for example, with respect to all pixels constituting the image using a predetermined filter coefficient. Can be set.

  Here, the result of extracting the feature points without applying the directional filter and the result of extracting the feature points after applying the differential filter will be described by comparing FIGS. 5 and 6.

  First, before comparing FIG. 5 and FIG. 6, each figure will be described.

  FIG. 5 is a diagram illustrating an example of a display screen when the feature points are extracted without applying the direction filter according to the present embodiment.

  FIG. 5A shows an image obtained by capturing the rear part of the automobile before the feature point is extracted without applying the direction filter. It can be seen that the rear part of the automobile has a gentle brightness gradient because it is monochromatic and has almost no pattern.

  FIG. 5B shows an image obtained by capturing the rear portion of the automobile when the feature points are extracted without applying the direction filter. The white points shown in FIG. 5B are the extracted feature points.

  FIG. 6 is a diagram illustrating an example of a display screen when the feature points are extracted after applying the direction filter according to the present embodiment.

  FIG. 6A shows an image obtained by imaging the rear part of the automobile after the direction filter is applied. As a result of applying the direction filter to the rear part of the automobile, the brightness gradient at the rear part of the automobile is clearly shown. It is shown.

  FIG. 6B shows an image obtained by capturing the rear part of the automobile when the feature points are extracted after applying the direction filter. Similar to FIG. 5B, the white point shown in FIG. 6B is the extracted feature point.

  Here, comparing the number of white spots shown in FIG. 5B and FIG. 6B, it can be seen that the number of white spots shown in FIG. 6B is larger. This indicates that, in images obtained by imaging the same object, more feature points can be extracted in the direction-filtered image.

  A frame 11 in FIG. 6A shows a display result after the direction filter is applied to a portion showing a gentle luminance gradient. A frame 12 in FIG. 6B shows the result of extracting the feature points at the same location as the frame 11. As described above, the feature point can be clearly extracted by applying the direction filter even in a portion showing a gentle luminance gradient.

  The filtering process is not performed on the entire screen, but is performed locally only on portions determined to have low reliability. Therefore, it is possible to stably acquire and track feature data even in a region where the luminance gradient is small.

  Further, since the differential filter and the directional filter are coefficients set in advance, the control unit 2 does not need to perform a calculation process in extracting the feature points. Therefore, image processing such as tracking can be performed without imposing an excessive processing load on the control unit 2.

  And the control part 2 extracts the feature point of an image by this filtering process, and calculates an optical flow etc.

  Returning to the description of FIG. 2, the control unit 2 ends the process when the eigenvalue is not less than or equal to the preset threshold value (step S <b> 3: NO).

  As described above, in the present embodiment, the control unit 2 assigns coordinate values to the pixel data constituting the image, extracts the luminance information of the pixel data corresponding to the coordinate values, and the luminance A characteristic value as a luminance gradient, which is the magnitude of the luminance indicated by the information, is calculated, and a filtering process is performed on the coordinate value where the characteristic value is equal to or less than a preset threshold value.

  Therefore, in order to obtain the motion of the feature points in two consecutive images, it is not necessary to perform calculation processing such as individually weighting the regions with low reliability of the feature points. Image processing such as tracking can be performed without imposing a heavy processing load.

  In addition, it is possible to detect camera shake even when a white wall, which is an area with a small luminance gradient, is used as a background. In addition, it can be applied to shake correction by acquiring the movement of a car that shakes. In an industrial product such as an automobile, there are many images including a plane (an image including an area having a small luminance gradient). Therefore, it is possible to perform tracking without overlooking the feature point by performing image processing on a part of the automobile.

  In the above-described embodiment, an example in which the present application is applied to an image display device has been described. However, for example, a digital video camera, a digital camera, a personal computer, a household electronic device, a surveillance camera, and the like. It can also be applied to an in-vehicle camera or a WEB camera.

DESCRIPTION OF SYMBOLS 1 Optical system engine 2 Control part 3 Display part S Image processing apparatus

Claims (3)

An extraction unit that assigns coordinate values to pixel data constituting an image and extracts luminance information of the pixel data corresponding to the coordinate values;
A calculation means for calculating an eigenvalue having a value corresponding to a feature point as the image of the pixel data to which the coordinate value is given based on the extracted luminance information;
Filtering processing means for performing a filtering process using a preset direction filter on the coordinate value whose calculated eigenvalue is equal to or less than a preset threshold;
An image processing apparatus comprising: An extraction step of assigning coordinate values to pixel data constituting the image and extracting luminance information of the pixel data corresponding to the coordinate values;
A calculation step of calculating an eigenvalue having a value corresponding to a feature point as the image of the pixel data to which the coordinate value is given based on the extracted luminance information;
A filtering process step of performing a filtering process using a preset directional filter on the coordinate value whose calculated eigenvalue is equal to or less than a preset threshold;
An image processing method comprising: A computer included in the image processing apparatus;
An extracting unit that assigns a coordinate value to pixel data constituting an image and extracts luminance information of the pixel data corresponding to the coordinate value;
A calculation means for calculating an eigenvalue having a value corresponding to a feature point as the image of the pixel data to which the coordinate value is given based on the extracted luminance information; and
Filtering processing means for applying a filtering process to the coordinate value whose calculated eigenvalue is equal to or less than a preset threshold value using a preset direction filter;
An image processing program that functions as an image processing program.