JP4513394B2 - Color image processing method and image processing apparatus - Google Patents

Description

The present invention is an image for defect inspection that detects a color defect of an object by processing a color image that is generated by imaging a solid object and includes density data of each color component of red, green, and blue. The present invention relates to a processing method and an image processing apparatus.

  In typical color image processing performed conventionally, by allowing a user to specify a predetermined color, three-dimensional image data obtained by combining density data of each color component is converted into a one-dimensional color difference with respect to the specified color. And processing using the size of the color difference data is performed (see Patent Document 1).

  In addition, when extracting a portion where a color abnormality has occurred as a defect, it is also proposed to register a normal color as a reference color in advance and obtain a color difference from the reference color for each pixel constituting the measurement target image. (See Patent Document 2).

JP-A-7-203476 Japanese Patent Laid-Open No. 11-108759

  FIG. 9 shows an outline of processing for replacing each pixel on a color image with one-dimensional color difference data in a relationship in a virtual space (hereinafter referred to as “color space”) with three axes corresponding to each color component. It is. The color designated by the user corresponds to a specific point Q in this color space. Here, when the color difference is expressed by the distance from the point Q in this color space, the colors corresponding to the points on the surface of the sphere centered on the point Q are all expressed by the same value. In FIG. 9, the value converted to each density value of 150, 100, and 50 with the value of the point Q being 255 is schematically shown in the outline of the cross section of the sphere.

  When the user designates a color, the color to be designated may vary depending on the user or the same user. When the designated color changes, the position of the point Q also changes, so that the color difference value in each pixel also differs. That is, as in Patent Documents 1 and 2, when the color at a predetermined position on the image is set as a reference color, the density distribution on the image converted as a color difference varies depending on the selection of the reference color. Furthermore, there is a possibility that even the measurement result of the object and the detection result of the defect may be changed due to such fluctuation of the density distribution.

In addition, since the conventional method uses a specific color as a reference color, it is difficult to cope with the case where the color of the object changes during the inspection .

The present invention has been made paying attention to each of the problems described above, and when detecting a color defect of a solid object , the extraction result and the measurement result are stabilized by eliminating the need for the user to specify a reference color. An object of the present invention is to easily cope with a case where the color of an object changes during the inspection .

The present invention is applied to a method of processing a color image composed of density data of red, green, and blue color components. In the image processing method according to the present invention, a color image generated by imaging a plain object is used as a processing target image, and the target object in the processing target image is extracted, and a portion outside the extraction range is included. Set the measurement target area so that it does not occur. Further, the measurement target area is divided into a plurality of small areas having a size corresponding to the defect to be extracted, an average color of each small area is obtained, and two adjacent small areas in the measurement target area are sequentially arranged. A process for obtaining a color difference of average colors between the combined small areas and a process for comparing the color difference with a predetermined threshold value are executed for each combination. Then, when a combination of small areas where the color difference exceeds the threshold is found, it is determined that the object has a color defect.

  In the above method, the color of each constituent pixel of the color image to be measured is represented by a combination of red, green, and blue density data. The average color can be obtained by averaging the density value of each pixel in the area for each color component. That is, it can be considered that the color indicated by the average density value of the red component, the average density value of the green component, and the average density value of the blue component is an average color.

  According to the above method, for a color image of an object whose color change is small, for example, the processing object image is converted into a monochrome image and the edges of the object are extracted, and then extracted to the original color image If you set the measurement target area so that the part outside the edge is not included, and divide this measurement target area into multiple small areas with the size corresponding to the defect to be detected, If there is a defect, a color difference exceeding a threshold value occurs between the average color of this area and the average color of the adjacent area.

The color difference between adjacent small regions can be obtained, for example, as the distance between the average colors in the color space described above. Alternatively, each average color may be converted into one point in the virtual space by brightness, saturation, and hue, and the distance between these points may be obtained. Alternatively, the color difference may be calculated by replacing each average color with chromaticity or luminance specified by the International Lighting Commission.
In either case, it is determined that the object has a color defect when the color difference between adjacent small regions exceeds the threshold value .

An image processing apparatus for carrying out the above method includes an image input means for inputting a color image composed of density data of red, green, and blue color components, and the image input means images a solid object. An extraction unit for extracting an object in the processing target image in response to the input of the generated color image as the processing target image, and a portion outside the range extracted by the extraction unit with respect to the processing target image Area setting means for dividing the measurement target area into a plurality of small areas having a size set in advance as a size according to the defect to be extracted; Calculating means for obtaining an average color for each small area, and then combining two adjacent small areas in order, and for each combination, calculating an average color difference between the small areas in the combination; Comparing the color differences determined by said calculating means with a predetermined threshold value, when the color difference is found a combination of a small region exceeding a threshold, and discriminating means for discriminating that there is a color defect on the object It has.

In the apparatus configured as described above , the image input means can be an interface circuit for the image pickup means for generating a color image. When the imaging means is an analog camera, the image input means can include an A / D conversion circuit that digitally converts density data of each color component.
The image input means can also be configured as a communication interface circuit for inputting a color image generated in advance from an external device or a drive device for reading out a color image stored in a predetermined storage medium.

The extraction means, area setting means, calculation means, and discrimination means are preferably configured by a computer in which a program is set. However, it is not always necessary to configure all the means by a computer, and some of the means can be configured by an ASIC (application specific IC).

The above-described image processing apparatus can be used for inspection for extracting defects that appear as color abnormalities. Further, the image processing apparatus can be provided with means for displaying the extraction processing result, the good / bad determination result, and the like, and outputting the result to the outside .

According to this invention, when determining the presence or absence of a color defect in a plain object, there is no need to specify the reference color of the object, so there is no possibility that the detection result of the defect will change due to a change in the designated color, Color defects can be detected stably . Moreover, even if the base color of the object changes, it can be easily handled.

  FIG. 1 shows a basic configuration of an image processing apparatus to which the present invention is applied. This image processing apparatus includes a camera 2 for generating a color image and a main body 1. The color image generated by the camera 2 is taken into the main body 1 to determine the presence / absence of a color abnormality part, Processes such as extracting the object contained in it are executed. The main body unit 1 includes a CPU 10, a memory 11 in which programs are stored, an output unit 18 for outputting processing results, and the like, and can be configured by a personal computer, for example.

  Furthermore, the main body 1 of this embodiment is provided with image memories 12, 13, and 14 for each color component of red (R), green (G), and blue (B). The image data of each color component output from the camera 2 is stored in the corresponding image memories 12, 13, and 14, respectively. Although not shown in this figure, an interface circuit, an A / D conversion circuit, and the like are provided in the input path to each of the image memories 12, 13, and 14. The output unit 18 includes a communication interface circuit for transmitting the processing result to an external device, a display interface circuit for displaying the processing result on a monitor (not shown), and the like.

In addition, in the main body 1, in order to register a reference image extracted from a color image of a model of an object, image memories 15, 16, 17 (hereinafter referred to as “reference image memories 15, 16, 17 ”).
The memory 11 includes an auxiliary storage device such as a hard disk in addition to the ROM and RAM. The reference image memories 15, 16, and 17 and the image memories 12, 13, and 14 can be physically set in the same storage device (the hard disk or the like).

  Hereinafter, four examples of processing that can be performed by the above-described image processing apparatus will be described, and detailed processing contents of each example will be described. The “image” referred to below is a combination of R, G, and B image data.

(1) Defect inspection 1
This inspection is to inspect whether or not a color different from the original color has occurred on the surface of a plain object due to defects such as dirt and spots. FIG. 2 shows the procedure of this inspection.

  This procedure starts when the image data from the camera 2 is stored in each of the image memories 12, 13, and 14. First, in the first step 1 (hereinafter, step is abbreviated as “ST”), a measurement target region is set for a target on an image. Although not described in detail in FIG. 2, when setting the measurement region, for example, a monochrome image is created from the input image data of each color component, and an edge of the monochrome image is extracted. Then, an image of the object is extracted. Then, based on the extraction result, a measurement target region is set in the original color image so as to include the inspection target portion of the target object and not to include a portion having another color such as a background portion. However, when the object does not greatly deviate, the setting position of the measurement target area may be registered in advance.

  In the next ST2, the measurement target area is divided into a plurality of small areas. The size of the small area is set in advance according to the size of the defect to be extracted. FIG. 3 shows a specific example, and a plurality of (12 in the illustrated example) small regions are set by equally dividing the measurement target region 20 for each of the x and y axes.

  In the next ST3, for each small area, the density data in each pixel in the small area is averaged for each color component of R, G, and B. The color represented by the average value of R, the average value of G, and the average value of B obtained by this averaging is the average color.

  Next, in ST4, the counter n is set to 1 and the maximum color difference value Dmax is set to 0, respectively. Then, paying attention to the n-th small area, the color difference Dn is calculated for the small area adjacent to the small area in any of the vertical and horizontal directions (ST5, ST6). This color difference Dn can be obtained as the Euclidean distance in the color space. That is, when the average color in the small area under consideration is (R0, G0, B0) and the average color in the adjacent small areas is (R1, G1, B1), the color difference Dn is expressed by the following equation (1). Can be calculated.

  In ST7, the color difference Dn obtained by the above equation (1) is compared with the maximum value Dmax. Since the initial value of Dmax is set to 0 in ST4, the first determination in ST7 is always “YES”. In this case, the process proceeds to ST8, and the maximum value Dmax is rewritten with the value of Dn.

  Thereafter, after performing ST6 to 8 on all the small regions adjacent to the small region of interest, the value of n is further updated to change the target of interest, and the above processing is performed (ST5 to 11). ). As a result, the color difference Dn is obtained for each set of adjacent small regions, and the maximum value among them is extracted as Dmax. In ST12, the maximum value Dmax is compared with a predetermined threshold value Dth. Note that the threshold value Dth can be set based on a color difference (desirably the maximum color difference) obtained from an image of an object having no defect prior to inspection.

  If it is determined in ST12 that Dmax is larger than the threshold value Dth, the process proceeds to ST13 and a defect determination (meaning that the object is defective) is performed. On the other hand, if Dmax is equal to or less than the threshold value Dth, the process proceeds to ST14 and a good determination (meaning that the object is free of defects) is performed. In ST15, the determination result of ST13 or ST14 is output from the output unit 18, and the process ends.

  According to the above processing, a defect inspection can be performed on a plain object without having to register the color of the object. Moreover, even when the color of the object changes during the inspection, the inspection can be promptly advanced by the same algorithm.

  In the procedure of FIG. 2, the maximum value Dmax of the color difference in the measurement target region is obtained and the presence or absence of a defect is determined based on the magnitude of the value of Dmax. Instead, each time the color difference Di is obtained, The value may be compared with the threshold value Dth, and the defect determination may be performed when the color difference Di exceeds the threshold value Dth.

  In addition, when a color difference exceeding the threshold value Dth is extracted, the position of the defect can be specified by checking the color difference with the other small areas for the set of small areas when the color difference is obtained. . For example, in the procedure of FIG. 2, when the maximum value Dmax finally obtained exceeds the threshold value Dth, the color difference with respect to the other small areas is checked for two small areas when Dmax is obtained, For all of the small areas, the small area from which the color difference exceeding the threshold value Dth or the color difference close to Dth is extracted is specified as the defective portion.

(2) Defect inspection 2
This inspection is to check whether or not a color that should not be originally included is included in an object in which characters, designs, patterns, and the like are represented by a plurality of types of colors, not plain. When performing this inspection, first, a model of an object having a good color distribution is imaged, an image in a predetermined region including the object is extracted from the obtained color image, and this is extracted from the reference image memory 15. , 16 and 17. Then, a color difference is obtained for each corresponding pixel between the image to be inspected and the reference image, and a pixel whose color difference exceeds a predetermined threshold value dn1 is extracted as a defective pixel.

  FIG. 4 shows the detailed procedure of the inspection. In ST101, which is the first step in this procedure, the edge of the object is extracted by converting the color image to be processed into a monochrome image, and the reference image and the color image to be processed are added to the color image to be processed based on the extraction result. Set the measurement target area of the same size. In the following description, it is assumed that the model and the target object are accurately aligned when the reference image is superimposed on the set measurement target region. In consideration of a subtle difference in size, it is desirable to include a slight error in the threshold value dn1.

When the measurement target region is set, in ST102, the counter i is initialized to 1, and the number of defective pixels dn is initialized to 0. In the next ST103, the color difference Di with respect to the corresponding pixel on the reference image side is calculated for the i-th pixel. Here, when the color difference Di exceeds the predetermined threshold value D1, ST104 becomes “YES”, the process proceeds to ST105, and the number of defective pixels dn is updated to one larger value.
Note that the color difference Di obtained in ST103 is expressed as the Euclidean distance of the color in the color space for one pixel in the measurement target region and the corresponding pixel on the reference image, and is the same method as the equation (1). Can be sought.

  Hereinafter, similarly, by updating the value of i, while paying attention to each pixel in the measurement target region in order, the color difference Di with the corresponding pixel on the reference image is calculated for the target pixel, and the color difference Di is calculated. The number of pixels exceeding the threshold value D1 is counted as the defective pixel number dn (ST103 to 107). When the processing for all the pixels is completed, the process proceeds to ST108, and the number of defective pixels dn is compared with a predetermined threshold value dn1. Here, when the number of defective pixels dn exceeds the threshold value dn1, the process proceeds to ST109 to perform defect determination. On the other hand, if the number of defective pixels dn is less than or equal to the threshold value dn1, the process proceeds to ST110 and a good determination is made. Finally, in ST111, the determination result is output and the process is terminated.

According to the defect inspection described above, a defect can be easily extracted without performing individual inspection for each color of an object in which a plurality of types of colors are mixed. In addition, since the extraction is performed in units of pixels, even minute defects can be extracted.
However, only an aggregate in which a predetermined number or more of defective pixels (pixels having the color difference dn exceeding the threshold value dn1) may be extracted as defects. Further, the position and size of the defect may be extracted and output based on the coordinates of the defective pixel and the size of the aggregate.

(3) Position Measurement This measurement process is performed in order to detect the boundary between the background and the object (that is, the position of the contour line of the object) when both the background and the object are plain. This measurement process can also be used when extracting an object for setting a measurement target area in the defect inspection 1 described above.

This measurement process is based on the premise that the position of the contour line of the object is known to some extent, and the measurement target region is set in a range that may correspond to the contour line. FIG. 5 shows an example of setting the measurement target region. In the measurement target image 30, a rectangular measurement target region 32 that is long in the direction crossing the outline of the target object 31 (in this example, the x-axis direction) is set. is doing. In this embodiment, a window 33 having a predetermined width w (the length of the window 33 in the y-axis direction is the same as that of the measurement target area 32) is set at one end (the left end in the illustrated example) of the measurement target area 32. To do. Then, while moving the window 33 in the right direction, the average color in the window 33 is obtained each time the window 33 is moved by a distance corresponding to the width w. Further, by analyzing the average color obtained at each position, the x coordinate of the contour line of the object 31 is obtained.
The example of FIG. 5 is for extracting a contour line along the y-axis direction, and when extracting a contour line along the x-axis direction, a range that may correspond to the contour line. Then, a measurement target region that is long in the y-axis direction is set, and the y-coordinate of the contour line is obtained.

FIG. 6 shows the detailed procedure of this measurement process. In this example, it is assumed that the position of the contour line along the y-axis direction of the object is extracted based on the example of FIG.
In ST201, which is the first step, the measurement target area 32 is set in the manner shown in FIG. In the next ST202, the window 33 is set at the left end of the set measurement target region 32. In ST203, an average color in the window 33 is obtained. Further, the average color in the window 33 is obtained each time the window 33 is moved to the right by the number of pixels corresponding to the width w (ST 203 to 205). The average color may be calculated every time the window 33 moves by one pixel.

  The average color acquired by scanning the window 33 is stored in the memory 11 in association with labels (for example, P1, P2, P3...) Including numbers indicating the acquired order. Further, the position of the window 33 when each average color is obtained is expressed as an x coordinate of a representative point (such as the left end point) of the window 33. This x coordinate is also a label (see FIG. For example, it is stored in the memory 11 in association with x1, x2, x3.

  When the window 33 moves to the right end of the measurement target region 32, in ST206, two average colors obtained at each position are combined, and the distance in the color space is calculated for each combination.

  In the next ST207, based on the distance calculation result, two colors located at a maximum distance in the color space are extracted, and a line segment L having these colors as endpoints is set.

  In the next ST208, the counter j is initialized to 1. This j corresponds to the average color label obtained by scanning the window. J = 1 at the time of initial setting corresponds to the average color acquired at the initial setting position of the window 33.

  In ST209, a perpendicular line from the point corresponding to the jth average color obtained to the line segment L is set in the color space. In ST210, the intersection of the perpendicular and the line segment L is extracted. In ST211, it is checked whether or not the intersection point exceeds the midpoint of the straight line L. Here, when it is determined that the middle point is not exceeded, j is incremented to focus on the average color obtained next, and ST209 to 211 are similarly executed.

  In the above processing, for any one of the average colors, when the intersection of the perpendicular line from the point corresponding to the average color and the line segment L exceeds the midpoint of the line segment L, the loop of ST209 to 212 is ended. Then, the process proceeds to ST213, and the position where the average color is obtained (the one corresponding to the label corresponding to the value of j among the set positions of the window) is determined as the position of the contour line. In ST214, the coordinates indicating the position of the boundary line are output as the determination result, and the process ends.

  FIG. 7 shows an example of the distribution of each average color in the color space. In this example, assuming that the window 33 is set at five locations, the points corresponding to the respective average colors are indicated as P1, P2, P3, P4, and P5 in the order of extraction. In the figure, O is the midpoint of the line segment L.

  When the measurement target region 32 is set so as to cross the outline of the target, one of the two colors having the largest distance is an average color when the window 33 is set as the background portion, and the other Can be considered to be the average color when the window 33 is set on the object. Further, while the window 33 passes the boundary between the background and the object, the ratio of both included in the window 32 is considered to change gradually. For this reason, it can be considered that the average color P1 acquired at the first set position and the average color P5 acquired at the last set position are the end points of the line segment L as shown in FIG.

  In the example of FIG. 7, when performing the processing of ST207 to 212, pay attention to the points P1, P2, P3, P4, and P5 corresponding to each average color in order, and the intersection of the perpendicular line from the point of interest to the line segment L Will be tracking. Here, the intersection point of the first point P1 is P1 itself, and the following intersection point moves toward the other end point P5 of the line segment L. In the example of FIG. 7, since the point P4 first exceeds the middle point O when viewed from the point P1, the position where the average color corresponding to this point P4 is obtained is determined as the position of the contour line. Note that the point for determining that the color has changed is not limited to the middle point O, and may be a point separated from the point P1 by an arbitrary distance (however, a point separated by a sufficient distance to be regarded as a change in color) There is a need to.).

  The above description is based on the premise that the measurement target region 32 is set so as to cross the outline of the target object. However, a misjudgment when the measurement target region 32 does not include an image of the target object. In order to prevent discrimination, when the line segment L is smaller than a predetermined threshold value, the processing may be stopped.

(4) Number inspection In this inspection, whether a predetermined number of objects are extracted by processing an image of an object in which a plurality of objects (for example, marks) including a plurality of kinds of colors are arranged. It is to determine whether or not. For this discrimination process, in this embodiment, an object having a model of an object having a good color distribution is imaged in advance, and an image including only one of the models is cut out from the obtained color image. Register as an image.

  Further, in this embodiment, after obtaining the average color for the reference image, the color of each pixel on the reference image is set to one point Mi in the color space, and the distance from the point Mμ indicating the average color to the point Mi And a direction from the point Mμ toward the point Mi. This distance and direction correspond to the length and direction of a vector starting from the point Mμ and ending at the point Mi. The direction can be expressed as an angle of the vector with respect to a reference plane (for example, a plane formed by the R axis and the G axis).

  The distance and direction obtained for each pixel are registered in the memory 11. At the time of inspection, a measurement target region having the same size as the reference image is scanned on the image to be inspected, and the similarity to the reference image is obtained using registered data in the memory 11 every time one pixel moves. Then, by counting the number of times that the degree of similarity exceeds a predetermined threshold C1, the number of objects is measured, and the suitability of the number is determined.

  FIG. 8 shows the detailed procedure of the inspection. In this procedure, (x, y) is the coordinates of each pixel of the image to be processed, ax indicates the number of pixels in the x-axis direction, and ay indicates the number of pixels in the y-axis direction. En is the number of extractions of the object.

  In ST301, which is the first step, the extraction numbers En and y are set to initial values 0, and in the next ST302, x is set to 0. In ST303, the measurement target region is set starting from the position (x, y). That is, the measurement target region is set so that the position of the coordinates (0, 0) is the upper left vertex.

  In the next ST304, an average color in the measurement target area is calculated. This average color is also represented by an average value for each of the R, G, and B color components, and can be represented as one point Pμ in the color space.

  The next ST305 is executed for each pixel in the measurement target region. Here, the color of the pixel to be processed is considered as one point Pi in the color space, and the distance from the point Pμ indicating the average color and the direction from the point Pμ to the point Pi are obtained. The distance and the direction correspond to the length and direction of the vector having the point Pμ as the start point and the point Pi as the end point, as in the case of the reference image.

  In ST306, using the distance and direction obtained in ST305 and the distance and direction of the reference image registered in the memory 11, the similarity C (x, y) is obtained based on the following equation (2). . In Equation (2), n is the total number of pixels in the measurement target area. D (Pi, Pμ) is the distance between the point Pi and the point Pμ, and d (Mi, Mμ) is the distance between the point Mi and Mμ on the reference image side. Further, θi is an angle formed by the vector formed by the points Pμ and Pi and the vector formed by the points Mμ and Mi, and is a numerical value in the range of 0 to 180 degrees.

  When the similarity C (x, y) is obtained in this way, in ST307, the similarity C (x, y) is compared with a predetermined threshold C1. If it is determined that the similarity C (x, y) exceeds the threshold value C1, the process proceeds to ST308, and the extraction number En is updated to one larger value.

  Thereafter, by moving the values of x and y in the ranges of 1 to ax and 1 to ay, respectively, the measurement target region is scanned pixel by pixel, and the correlation value C (x, y is obtained for each scan by the same processing as described above. ) And the number of times that the correlation value C (x, y) exceeds the threshold value C1 is counted as the extraction number En (ST302 to 312). When the scanning is completed, the process proceeds to ST313, and it is checked whether or not the extraction number En matches the reference number E0 registered in advance. If En = E0, the process proceeds to ST314, and it is determined that the object to be inspected is a non-defective product. On the other hand, if En ≠ E0, the process proceeds to ST315, and it is determined that the object to be inspected is a defective product. Thereafter, the process proceeds to ST316, and the determination result is output and the process is terminated.

  According to the above equation (2), the difference C (x, y) increases as the difference between d (Pi, Pμ) and d (Mi, Mμ) decreases and θi approaches 0. . Therefore, by setting the threshold value C1 to a sufficiently high value, an image region close to the color distribution state of the reference image can be extracted as an object.

  However, in equation (2), if the relative color change with respect to the average color is the same, the similarity C (x, y) also increases, so that a region having a different hue from the reference image is also extracted as an object. It will be. When it is necessary to extract only a region having the same hue as that of the reference image, the similarity C (x, y) may be corrected by the following equation (3).

In equation (3), dmax is the maximum distance that can be obtained in the color space. For example, when the density value of each color component is represented by 8 bits, dmax = (255 ² × 3). d (Pμ, Mμ) is a distance between the average color Pμ obtained from the measurement target region and the average color Mμ obtained from the reference image. According to the corrected similarity C ′ (x, y) according to the equation (3), even if the relationship of the colors Pi in the measurement target region to the average color Pμ is similar to the relationship in the reference image, the average color The similarity value decreases as Pμ moves away from the reference average color Mμ.

  In the procedure of FIG. 8, the number of objects is obtained from the number of times the degree of similarity C (x, y) exceeds the threshold value C1, but instead, the degree of similarity C (x, y) is calculated. The value of (x, y) when the threshold value C1 is exceeded may be output as the position of the object. Also, the suitability of the color distribution of the object can be determined based on the value of the similarity C (x, y).

It is a block diagram which shows the structural example of the image processing apparatus concerning this invention. It is a flowchart which shows the procedure of the defect inspection 1. It is a figure which shows the example of a division | segmentation of a measurement object area | region. It is a flowchart which shows the procedure of the defect inspection 2. FIG. It is a figure which shows the example of a setting of the measurement object area | region and window for a position measurement process. It is a flowchart which shows the procedure of a position measurement process. It is a figure which shows the specific example of the process of ST207-211 of FIG. It is a flowchart which shows the procedure of number inspection. It is a figure which shows the range in which the same value is set in the conversion process based on a color difference.

1 Body 2 Camera 10 CPU
11 Memory 12, 13, 14 Image memory 14, 15, 17 Reference image memory

Claims (2)

In a method of processing a color image composed of density data of red, green and blue color components,
Using a color image generated by imaging a plain object as a processing target image, extract the target object in this processing target image and set the measurement target area so that no part outside this extraction range is included And
The measurement target area is divided into a plurality of small areas having a size corresponding to the defect to be extracted, and an average color of each small area is obtained,
A process of sequentially combining two small areas that are adjacent to each other in the measurement target area, obtaining a color difference of an average color between the combined small areas, and a process of comparing the color difference with a predetermined threshold value. It is executed for each combination, and when a combination of small areas where the color difference exceeds a threshold is found, it is determined that the object has a color defect.
A method for processing a color image. Image input means for inputting a color image composed of density data of each color component of red, green, and blue as a processing target image;
An extracting means for extracting a target object in the processing target image in response to inputting a color image generated by imaging the solid target by the image input means as a processing target image;
After setting the measurement target area so that a portion outside the range extracted by the extraction unit is not included in the processing target image, the measurement target area is set in advance as a size corresponding to the defect to be extracted. Area setting means for dividing into a plurality of small areas of a set size;
After obtaining the average color for each small area, combining two small areas in an adjacent relationship in order, and for each combination, calculating means for obtaining a color difference of the average color between the small areas for the combination;
A comparing means for comparing the color difference obtained by the calculating means with a predetermined threshold value, and determining a color defect in the object when a combination of small areas where the color difference exceeds the threshold value is found; An image processing apparatus.

Images