JP2017153054A - Coloring inspection apparatus and coloring inspection method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a coloring inspection apparatus capable of increasing the accuracy in a comparison work of textures such metallic touch or pearl-twinkling.SOLUTION: The coloring inspection apparatus carries out the steps of: identifying the center of a distribution in a Lab color space histogram (S145); shifting (mapping) entire distribution of the Lab color space histogram of any one of two U(L, a, b) and U(L, a, b) by a difference of ΔA, ΔB and ΔL of the center coordinate so that the center coordinate coincide with the center coordinate (S146); and calculating a texture spreading index which represents a difference in spatial spreading (S147). By calculating the spatial spreading in a three-dimensional space of the Lab color space histogram distribution, a difference in a metallic touch only can be extracted and quantified, and depending on the difference in the spreading, a difference in twinkling of a brightness material can be independently detected excluding the color.SELECTED DRAWING: Figure 14

Description

  The present invention relates to a color inspection apparatus and method for inspecting the color distribution of an image in order to evaluate coloring such as painting of a product.

  Conventionally, the color of the paint is generally adjusted by the department in charge of the industrial manufacturer, creating an original painted on a standard board, and the paint manufacturer makes the paint color the same as this original color. To provide paints to industrial manufacturers. The present condition is that the paint is adjusted until the OK from the industrial manufacturer is given by the intuition and experience of the paint manufacturer's engineers.

  By the way, regarding the adjustment of color and texture, the invention of Patent Document 1 has been proposed. In the present invention, a camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalent to the CIE XYZ color matching function, and three spectral sensitivities acquired by the camera are obtained. An arithmetic processing unit that obtains and calculates coloring data obtained by converting the image having tristimulus values X, Y, and Z in the CIE XYZ color system, and an illumination unit that irradiates the measurement object. Among the coloring data obtained by imaging the measurement object, the specified inspection area is set, and the inspection object and the reference object are measured from the X, Y, and Z values of each pixel in the inspection area. By calculating the normalized x and y values for the inspection region, dividing the inspection region of the xy coordinates of the xy chromaticity diagram by a grid, and adding the number of pixels of the inspection object and the reference object belonging to each lattice, xy Create a chromaticity coordinate histogram distribution, or Generates an XYZ chromaticity coordinate histogram distribution by accumulating the number of pixels of the XYZ chromaticity coordinate histogram acquired in the three-dimensional XYZ coordinates, and generates two xy chromaticity coordinate histogram distributions or XYZ colors of the inspection object and the reference object The color and its texture are inspected by calculating the Lab average value and the spread degree difference calculation indicating the overlapping ratio of the degree coordinate histogram distribution.

  Thus, in order to comprehensively inspect the color and texture, it is a method that is correlated with the human eye. For example, it is used in an in-line camera device for industrial products. When an industrial product flows, it is used to detect the color difference between the whole or part of the industrial product with the same judgment as the human eye in real time. In this way, defective coloring can be excluded based on the same judgment criteria as those of the human eye.

JP 2014-187558 A

  However, in the invention shown in Patent Document 1, since it is a comprehensive judgment based on the color matching index as a parameter for adjusting the color and texture, it is convenient for in-line color inspection, but this is used. It can be difficult.

  For example, in the adjustment of the coating of an industrial product, generally, the color of a paint for an industrial product planned by a related department of a manufacturer is determined by various examinations, and this becomes the standard color of the product. Next, make a color sample that is actually painted, and make it the original. A paint-related company (for example, a paint painting company, a sheet metal painting company, etc.) manufactures the same color paint as the original.

  It is difficult to bring this color matching work completely close, and since colors are matched with the feeling and experience of engineers, considerable experience and skill are required. Eventually, with the approval of an industrial manufacturer, this color matching is a very difficult and troublesome task.

  In particular, the metallic feeling of the glittering material placed on the paint is the most troublesome problem, and in order to determine how strong or weak the metallic feeling is, the comprehensive index grasps it. I can't.

  There is a strong demand for necessary parameters to support such difficult color adjustment.

  There are various materials such as metal materials, urethane materials, plastic materials, carbon fiber materials, etc., and the color judgment does not change so much, but the metallic feeling changes considerably, so the color matching index Then, it may be difficult to accurately determine the metallic feeling.

  SUMMARY OF THE INVENTION An object of the present invention is to provide a method for separating color and texture and presenting each shift independently, thereby facilitating color and texture adjustment work.

  In view of the above-described problems, the present invention provides a camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to a CIE XYZ color matching function, and the camera An arithmetic processing unit that acquires and calculates coloring data obtained by converting an image having three spectral sensitivities into tristimulus values X, Y, and Z in the CIE XYZ color system, and an illumination unit that irradiates the measurement object. The arithmetic processing unit sets the specified inspection area among the coloring data obtained by imaging the measurement object, and divides the inspection area of coordinates corresponding to the color space of the XYZ color system with a grid. Integrate the number of pixels of the inspection object and reference object belonging to each grid to create a color space histogram distribution of the XYZ color system, and specify the center of the two color space histogram distributions of the inspection object and reference object And either color space A coloring inspection apparatus that calculates a texture spread index indicating a difference in spread of a color space histogram distribution by shifting the center of the histogram distribution so as to be close to the center of another color space histogram distribution.

  The present invention relates to a color inspection method using a camera having three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) linearly converted equivalently to a CIE XYZ color matching function under illumination. A step of generating coloring data obtained by converting an image having three spectral sensitivities acquired by imaging with the camera into tristimulus values X, Y, and Z in the CIE XYZ color system, and obtained by imaging the measurement object. Among the coloring data, a step of setting the specified inspection area, and a coordinate inspection area corresponding to the color space of the XYZ color system, respectively, are partitioned by a grid, and the inspection object and the reference object belonging to each grid A step of creating a color space histogram distribution by integrating the number of pixels, and specifying a center of the two color space histogram distributions of the inspection object and the reference object, A color inspection comprising: shifting the cloth center so as to be close to the center of another color space histogram distribution, and calculating a texture spread index indicating a difference in the spread of the color space histogram distribution. Is the method.

  In the claims, the XYZ color system means a broad meaning including other CIE color systems, and means a narrow meaning except in the claims. The XYZ color system in the narrow sense is an RGB color system defined so that negative values do not appear by simple primary conversion, and other CIE color systems such as Yxy, XYZ, Lab, Luv, etc. This is a concept including a two-dimensional chromaticity diagram or a three-dimensional color space. Therefore, the XYZ color system in a broad sense includes the XYZ color system in a narrow sense and other CIE color systems developed from the XYZ color system.

  The XYZ color system in a narrow sense is defined by CIE in 1931 at the same time as the RGB color system so that negative values do not appear in the RGB color system by simple primary conversion.

  The xyY color system (also referred to as the Yxy color system) is defined in order to express an absolute hue from the XYZ color system because the relationship between numerical values and colors is difficult to understand in the XYZ color system. .

The Luv color system is one of the uniform color spaces established by the CIE in 1976, and CIE L ^* u ^* v ^* is based on the wavelength of light, and the wavelength interval of the XY chromaticity diagram of the XYZ color system is equal. Improved. In Japan, it is specified in JIS Z8518.

The Lab color system is CIEL ^* a ^* b ^* and is derived from the XYZ color system in order to measure a color difference due to a difference between perception and apparatus. In Japan, it is specified in JIS Z 8729.

  The XYZ color system in a broad sense includes a color space defined by two-dimensional coordinates and three-dimensional coordinates. As typical examples of the color space, there are configuration examples such as an XYZ color space and a Lab color space. In the case of a two-dimensional color space, for example, in the case of a Yxy color space or a Luv color space, an xy chromaticity diagram that is a two-dimensional plane (xy chromaticity value (plane) normalized in the Yxy color space), uv chromaticity diagram , U′v ′ chromaticity diagram. An xy chromaticity histogram distribution or a Luv chromaticity histogram distribution expressed as the density of pixels in a two-dimensional chromaticity diagram on a plane corresponds. In the case of a three-dimensional color space, for example, in the case of an XYZ color space or a Lab color space, an XYZ color space or a Lab color space that is a three-dimensional space may be mentioned. An XYZ color space histogram expressed as a density of pixels in a three-dimensional color space, a Lab color space histogram distribution, or the like corresponds.

  In contrast to the separation of color and texture on the two-dimensional xy chromaticity plane, uv chromaticity diagram, and u'v 'chromaticity diagram, other XYZ color spaces and Lab color spaces have colors in the three-dimensional color space. It becomes the separation of the texture. Therefore, the terms are defined separately from the xy chromaticity histogram, such as an XYZ color space histogram and a Lab color space histogram.

  The XYZ color space histogram and the Lab color space histogram are different from each other, and the texture spread index calculation in the Lab color space is performed by converting the XYZ color data into Lab chromaticity data and calculating from the converted data. To do.

  Examples of the coloring mode include coloring with a paint, a bright material, a staining solution, and the like.

  As for the mode of shifting so as to be close, for example, the centers of the histogram distribution are matched, the centers are close to each other within a predetermined range, are moved close to each other in parallel with the coordinate axis, and are linearly moved from the center to another center. As long as an appropriate index can be obtained, for example, it can be implemented in various modes.

  The index can be in various forms, not limited to a plane or a solid, such as an index, a graph, a picture, or a combination thereof.

  According to the technically related invention, a camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly transformed equivalent to a CIE XYZ color matching function, An arithmetic processing unit that acquires and calculates coloring data obtained by converting the acquired image having three spectral sensitivities into tristimulus values X, Y, and Z in the CIE XYZ color system, and an illumination unit that irradiates the measurement object. The arithmetic processing unit sets the specified inspection area among the coloring data obtained by imaging the measurement object, and for the inspection object and the reference object as the measurement object, respectively, The X, Y, and Z values of each pixel are calculated, and an inspection area of xy coordinates, XYZ coordinates, or Lab coordinates corresponding to the xy chromaticity diagram, XYZ chromaticity diagram, or Lab chromaticity diagram is partitioned by a grid. The inspection object and reference object belonging to each grid The xy chromaticity histogram, the XYZ color space histogram, or the Lab color space histogram distribution is created by integrating the number of pixels, and the two xy chromaticity histograms, the XYZ color space histogram, or the Lab of the inspection object and the reference object are created. The center of the color space histogram distribution is specified, and the center of any one of the xy chromaticity histogram, the XYZ color space histogram, or the Lab color space histogram distribution is used as another xy chromaticity histogram, the XYZ color space histogram, or the Lab color space. The color inspection apparatus is characterized by calculating a texture spread index of an xy chromaticity histogram distribution, an XYZ color space histogram distribution, or a Lab color space histogram distribution by shifting so as to be close to the center of the histogram distribution.

  According to the technically related invention, a color inspection using a camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly transformed equivalent to the CIE XYZ color matching function In the method, a step of generating coloring data obtained by converting an image having three spectral sensitivities acquired by imaging with the camera under illumination into tristimulus values X, Y, and Z in a CIE XYZ color system, and the measurement object Among the coloring data obtained by imaging an object, the step of setting the specified inspection area, and the X and Y values of each pixel in the inspection area for the inspection object and the reference object as measurement objects, respectively. And an inspection area of xy coordinates, XYZ coordinates, or Lab coordinates corresponding to the xy chromaticity diagram, the XYZ chromaticity diagram, or the Lab chromaticity diagram, respectively, are partitioned by a lattice, and the inspection belonging to each lattice is performed. Creating an xy chromaticity histogram, an XYZ color space histogram, or a Lab color space histogram distribution by integrating the number of pixels of the inspection object and the reference object; and two xy chromaticity histograms of the inspection object and the reference object; The center of the XYZ color space histogram or Lab color space histogram distribution is specified, and the center of one of the xy chromaticity histogram, XYZ color space histogram, or Lab color space histogram distribution is set to the other xy chromaticity histogram, XYZ color Shifting the space histogram or the Lab color space histogram distribution closer to the center and calculating a material spread index of the xy chromaticity histogram distribution, the XYZ color space histogram distribution, or the Lab color space histogram distribution. This is a coloring inspection method.

The image pickup apparatus according to the present invention picks up an image with three spectral sensitivities (S ₁ (λ), S ₂ (λ), S ₃ (λ)), that is, the observation object is divided into three channels. As the means, any of an optical filter set to obtain these spectral sensitivities, a dichroic mirror, a dichroic prism, or the like can be used.

Spectral sensitivities (S ₁ (λ), S ₂ (λ), S ₃ (λ)) of the imaging device are mountain-shaped with a single peak having no negative value from the CIE XYZ spectral characteristics. Equivalent conversion is performed under the condition that the peak values of the sensitivity curves are equal and the overlap of the spectral sensitivity curves is minimized. The curve of the spectral characteristic S ₁ has a peak wavelength of 582 nm and a half-value width of 523. ˜629 nm, and 1/10 width is 491 to 663 nm. Curve of the spectral characteristics _{S 2} is a peak wavelength of 543 nm, the half value width is 506~589nm, 1/10 width is 464～632Nm. Curve of the spectral characteristics _{S 3} is a peak wavelength of 446 nm, the half value width is 423~478nm, 1/10 width is 409～508Nm.

  In the present invention, the difference between the color and the texture such as the metallic feeling is separately presented, so that the color difference can be accurately adjusted, and the difference in the texture is qualified for the texture adjustment. Policy can be given. For example, as the metallic feeling increases, the histogram distribution of the XYZ color system tends to widen. Therefore, the difference between the spread histogram distribution and the histogram spread that does not spread, or the matching point guideline can be expressed. it can. Therefore, the color matching operation between the actual coloring (paint color, etc.) and the original standard color becomes very easy.

1 is a block diagram of a color inspection apparatus 1 according to Embodiment 1 of the present invention. It is a function which shows the spectral sensitivity of the camera 2 which is an XYZ color system camera in Embodiment 1 of this invention. This is a specific example of a method for acquiring image information according to three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) in Embodiment 1 of the present invention. (A) is explanatory drawing in the case of using a dichroic mirror. (B) is explanatory drawing in the case of using a filter turret. (C) is explanatory drawing at the time of attaching optical filter 22a, 22b, 22c to the image pick-up element 23 microscopically. It is a flowchart in the camera 2 of Embodiment 1 of the present invention. It is a flowchart in the arithmetic processing unit 3 of Embodiment 1 of this invention. It is a sub chart in the arithmetic processing unit 3 of Embodiment 1 of this invention. It is explanatory drawing which shows the shift process in xy coordinate space in the arithmetic processing unit 3 of Embodiment 1 of this invention. (A) is explanatory drawing which shows the test | inspection area | region T in the arithmetic processing unit 1 of invention Embodiment 1, (b) is xy chromaticity diagram which shows the chromaticity area | region K on the chromaticity diagram corresponding to the test | inspection area | region T, (c) ) Is an explanatory diagram of a chromaticity region K partitioned by a grid G, (d) is a schematic diagram showing a state of chromaticity overlapping on an xy two-dimensional chromaticity diagram, and (e) is an explanatory diagram showing a minimum distribution, (F) is explanatory drawing which shows an example (the numerical value of a square is a count value of a pixel) of xy chromaticity histogram distribution. (A) is explanatory drawing which shows the metallic degree of a measuring object, (b) is xy chromaticity histogram distribution map, (c) is a three-dimensional image figure of xy chromaticity histogram distribution. It is a block diagram which shows the structure of the coloring test | inspection apparatus 101 of this invention Embodiment 2. FIG. It is a flowchart (XYZ color space distribution) in the arithmetic processing unit 103 of the coloring inspection apparatus 101 of Embodiment 2 of the present invention. It is a flowchart (Lab color space distribution) in the arithmetic processing unit 103 of the coloring inspection apparatus 101 of Embodiment 2 of the present invention. It is explanatory drawing which shows the shift process in XYZ coordinate space of the coloring test | inspection apparatus 101 of this invention Embodiment 2. FIG. It is a block diagram which shows the structure of the coloring test | inspection apparatus 201 of this invention Embodiment 3. FIG. It is explanatory drawing which shows the shift process in the Lab coordinate space of the coloring test | inspection apparatus 201 of this invention Embodiment 3. FIG. It is explanatory drawing which shows the process by the coloring test | inspection apparatus of Embodiment 4 of this invention.

  A color inspection apparatus 1 according to a preferred embodiment 1 of the present invention will be described with reference to FIGS.

  The color inspection apparatus 1 includes a camera 2 having three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) linearly converted equivalently to the CIE XYZ color matching functions, and three images acquired by the camera 2. An arithmetic processing unit 3 that acquires and calculates coloring data obtained by converting an image having two spectral sensitivities into tristimulus values X, Y, and Z in the CIE XYZ color system, and an illumination unit 6 that irradiates a color sample 5 The arithmetic processing unit 3 sets the specified inspection area among the coloring data obtained by imaging the color sample 5, and as the color sample 5, for each of the inspection object and the reference object, each pixel in the inspection area is set. X, Y, Z values are converted into Lab values, the average value of each value of Lab is calculated, the inspection area of the xy coordinates in the xy chromaticity diagram is partitioned by a grid, and the inspection object and reference object belonging to each grid are Create a Lab color space histogram distribution by accumulating the number of pixels. Identify the center of the two xy chromaticity histogram distributions of the inspection object and the reference object, and set the center of one of the xy chromaticity histograms, the XYZ color space histogram, or the Lab color space histogram distribution of the other xy chromaticity histogram distributions. The shift is performed so as to coincide with the center, and the spread degree difference of the xy chromaticity histogram distribution is calculated. Examples of the color sample 5 include a paint color plate at a paint manufacturer.

  Since the appearance differs depending on the angle due to the flip-flop, the camera 2 is manually moved here and images are taken from at least three different angles. There is an illuminating unit 6, the camera 2 is installed underneath, and the camera 2 can be manually changed in angle. The camera 2 can measure the paint color of the color sample 5 and its Lab color histogram distribution data from multiple angles.

The spectral sensitivity of the camera 2 satisfies the router condition, and the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is negative from the XYZ color matching function as shown in FIG. It is a mountain shape having no value and a single peak, and is equivalently converted from the condition that the peak values of the respective spectral sensitivity curves are equal and the overlapping of spectral sensitivity curves is minimized. Specifically, the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) have the following characteristics.
Peak wavelength Half width 1/10 width S1 582nm 523-629nm 491-663nm
S2 543nm 506-589nm 464-632nm
S3 446 nm 423-478 nm 409-508 nm

  The peak wavelength of the spectral characteristic S1 can be handled as 580 ± 4 nm, the peak wavelength of the spectral characteristic S2 is 543 ± 3 nm, and the peak wavelength of the spectral characteristic S3 can be handled as 446 ± 7 nm.

The three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are obtained using the following Equation 1. For details on the spectral characteristics themselves, refer to Japanese Patent Application Laid-Open No. 2005-257827.

  The specifications of the camera 2 are, for example, an effective frequency value of about 5 million pixels, an effective area of 9.93 mm × 8.7 mm, an image size of 3.45 μm × 3.45 μm, a video output of 12 bits, a camera interface GigE, and the number of frames (during focus adjustment) 3-7 Frame / Sec, shutter speed 1 / 15,600Sec ~ 1 / 15Sec, integration time up to 3 seconds, S / N ratio 60dB or more, lens mount F mount, operating temperature 0 ℃ -40 ℃, operating humidity 20% -80% .

  As shown in FIG. 1, the camera 2 is disposed behind the photographing lens 21, the three optical filters 22a, 22b, and 22c disposed behind the photographing lens 21, and the optical filters 22a, 22b, and 22c. An image sensor 23 (CCD, CMOS, etc.). The three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) of the camera 2 are given by the product of the spectral transmittance of the optical filters 22a, 22b, and 22c and the spectral sensitivity of the image sensor 23. It is. The arrangement relationship between the optical filters 22a, 22b, and 22c and the image sensor 23 in FIG. 1 is merely shown schematically. Specific examples of the method of acquiring image information according to the three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) will be given below. In the first embodiment, any of these may be adopted. It is also possible to adopt other methods.

  FIG. 3A shows a system using a dichroic mirror. This is because light of a specific wavelength is reflected by the dichroic mirror 22c ′, and the remaining light that has been transmitted is further reflected by another dichroic mirror 22a ′ to be spectrally separated, and the image pickup devices 23a, 23b. , 23c are read in parallel. Here, the dichroic mirror 22a ′ corresponds to the optical filters 22a and 22b, and the dichroic mirror 22c ′ corresponds to the optical filter 22c. Light incident from the photographic lens 21 is reflected by the dichroic mirror 22c ′ according to the spectral sensitivity S3, and the remaining light is transmitted. The light reflected by the dichroic mirror 22c ′ is reflected by the reflecting mirror 26, and the spectral sensitivity S3 is obtained by the imaging device 23c. On the other hand, the light transmitted through the dichroic mirror 22c ′ is reflected by the dichroic mirror 22a ′, and the light according to the spectral sensitivity S1 is reflected, and the remaining light according to the spectral sensitivity S2 is transmitted. To obtain the spectral sensitivities S1 and S2. Instead of the dichroic mirror, a dichroic prism having the same characteristics may be used to split the light into three, and the image sensors 23a, 23b, and 23c may be bonded to the positions where each light is transmitted.

  The system shown in FIG. 3B uses a filter turret 27. Optical filters 22a, 22b, and 22c are provided on a filter turret 27 having the same direction as the incident light from the photographing lens 21 as a rotation axis, and these are mechanically rotated. S1, S2, and S3 are obtained.

  FIG. 3C shows a system in which the optical filters 22 a, 22 b, and 22 c are microscopically attached to the image sensor 23. The optical filters 22a, 22b, and 22c on the image sensor 23 are provided in a Bayer array type. This arrangement is such that the optical filter 22b is provided in half of the area on the image sensor 23 divided into a grid, and the optical filter 22a and the optical filter 22c are equally arranged in the remaining half of the area. That is, the arrangement amount is optical filter 22a: optical filter 22b: optical filter 22c = 1: 2: 1. The arrangement of the optical filters 22a, 22b, and 22c other than the Bayer arrangement is not particularly hindered in the first embodiment. Each of the optical filters 22a, 22b, and 22c is very fine and is attached to the image sensor 23 by printing. However, in the present invention, this arrangement is not meaningful, but a filter having characteristics of spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is attached to the image sensor.

  The camera 2 transmits image information acquired by spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) to the arithmetic processing device 3, and the arithmetic processing device 3 uses the tristimulus values X, There is provided a display device (not shown) for converting the image data to Y and Z, performing arithmetic processing by conversion processing on the acquired image data of the tristimulus values X, Y, and Z, and displaying the visualized image.

  The arithmetic processing unit 3 calculates the luminance, chromaticity, etc. at an arbitrary position of the image acquired by the camera 2 and performs a visualization process. Illumination is applied obliquely from the color sample 5, and the Lab color space histogram distribution data of the paint color is compared and indexed.

  The color sample 5 is usually imaged at one place by the camera 2, and the camera 2 moves and images at another angle as necessary. Here, for example, it is possible to shoot at three locations (an appropriate number of locations may be used) at 45 degrees on the front and left and right.

  The illumination source of the illumination unit 6 employs a xenon lamp (pseudo sunlight). The illumination unit 6 includes a Fresnel lens assembly in addition to the xenon lamp. The xenon lamp is illuminated uniformly from above the color sample 5. In addition to xenon lamps, LED artificial solar lights may be used.

  The display device 7 is connected to the arithmetic processing device 3, receives an image signal processed by the arithmetic processing device 3, and displays an image on a screen. The arithmetic processing device 3 or the display device 7 includes input means (not shown) and the like as appropriate. The input means is a keyboard, a mouse, a touch panel provided in the image display device, or the like.

  The operation of the color inspection apparatus 1 will be described with a specific example. As shown in FIG. 1, the color inspection device 1 operates by connecting a camera 2, an arithmetic processing device 3, and a display device 7. The connection method can be selected regardless of wired or wireless. The flowchart in the camera 2 is shown in FIG. 4, and the flowchart in the arithmetic processing unit 3 is shown in FIG.

  When the camera 2 is turned on, initialization is performed as shown in FIG. 4 (initialization S1). Next, the color sample 5 is imaged by the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) (imaging processing S2), and then the imaged image data is input by the image sensor 23 (input). Processing S3), the processing unit 3 converts the tristimulus values X, Y, and Z (conversion processing S4). Spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are transmitted to the display device 7 (data transmission S5). When the image is a moving image, a series of processing from imaging processing S2 to data transmission S5 is continuously performed. The image is displayed on the image display device 7.

  In the imaging process S2, an example of measuring various color samples 5 is given, but the color sample 5 is imaged by the camera 2 at different angles for specific areas where the imaging positions are different. There are a plurality of imaging locations, and the number can be selected as appropriate. Here, the measurement is made from three directions of front (0 degree), left 45 degrees, and right 45 degrees. Further, the measurement location is such that the 0-degree optical axis of the camera 2 is perpendicular to the body surface of the color sample 5. In addition, the illumination is characterized by illumination from obliquely above as with sunlight.

  Expressions for converting the tristimulus values X, Y, Z to the Y′xy color system are shown in Formulas 2 and 3, respectively. Here, a luminance meter (not shown) is used together with the camera 2, and Y is Y ′ calibrated by the luminance meter value (nt). Since color space conversion formulas are commonly used, other detailed formulas are omitted.

The XYZ color system is currently the basis of each color system as the CIE standard color system. It was developed based on the principle of additive color mixing of the three primary colors of light (R = red, G = green, B = blue-violet), and the color is represented by three values of Yxy using a chromaticity diagram. Y corresponds to lightness by reflectance, and xy becomes chromaticity.

  The imaging process S2 is a step of imaging the color sample 5 by the camera 2 having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) (see FIGS. 1 and 4). Spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are given according to the above formula 1. Input processing S3 is continuously performed at the same time as imaging is performed by the imaging lens 21, the optical filters 22a, 22b, and 22c, and the imaging device 23.

  Since the input image data is a value according to the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)), the image data captured by the conversion processing S4 in the arithmetic processing unit 3 of the camera 2 is used. The image data is converted into tristimulus values X, Y, and Z. This conversion is performed according to Equation 1. That is, the tristimulus values X, Y, and Z are obtained by multiplying the inverse matrix of the coefficients in Equation 1. The camera 2 transmits the values to the arithmetic processing unit 3 with values according to the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)).

  When the processing unit 3 is turned on, initialization is performed as shown in FIG. 5 (initialization S110). The display device 7 receives the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) transmitted from the camera 2 while being connected to the camera 2 (data reception S120). Thereafter, the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) is converted into tristimulus values X, Y, Z (S140). The contents are transmitted to the display device 7 (display process S150). In accordance with the data reception S120 from the camera 2, a series of processing from the conversion processing S130 to the display processing S150 is continuously performed.

  The calculation process S140 is a step of calculating and visualizing the Lab average value, ΔL, Δa, Δb, ΔE, and xy texture spread index of the captured image, and when necessary to display on the display device 7, The color information is converted into RGB or the like.

  The display process S150 is a process of displaying the visualized texture spread index on the image display device, and the process returns.

  A sub-flowchart of S140 in FIG. 6 will be described. A first image of the reference object is captured, a second image of the inspection object to be compared next is captured, and a texture spread index is sequentially calculated as follows. The similarity of the texture is determined by the texture spread index obtained by separating the texture.

  An inspection region K (see FIG. 20B) corresponding to a region T (see FIG. 8A) to be inspected with respect to the captured image AB is set (step S141). Size and place can be set freely.

  The chromaticity xy is calculated to obtain the chromaticity Yxy (S142).

  An xy chromaticity histogram distribution of the reference object Q in the region K cut out from the imaged image A of the inspection object is created (S143). This chromaticity histogram distribution is an integrated number obtained by counting pixels belonging to the overlapping region D of the two histogram distributions shown in FIG.

  The xy chromaticity histogram distribution is a three-dimensional histogram showing the cumulative number of pixels belonging to each unit grid, and an overlapping region D is shown in FIG.

  As shown in FIG. 8C, the color distribution to be compared at the position of the xy coordinates is written in a plane, the inspection area K is partitioned by a grid G, and the pixels having the xy values of the sections are integrated. Then, a histogram distribution with the z axis is created. Let xy coordinates be a grid (solid grid) of a specific width, for example, a plane lattice obtained by cutting xy by 1/1000 (1000 lines). The histogram is scanned from end to end, and for each region partitioned into the grid G, the number of pixels belonging to this is scanned on the same xy plane and integrated in the z direction. In addition, if only the vertical plane or the horizontal plane within a specific range is calculated with the xy coordinates in the inspection region K, the calculation time can be shortened. If the grid cells are made finer, the accuracy will be improved, but the calculation time will be longer.

  Similarly to S143, an xy chromaticity histogram distribution of the image B of the reference object R is created (S144). The xy chromaticity histogram distribution is the cumulative number of pixels, and FIG.

  For each of the Lab's a-axis, b-axis, and L-axis, the sum of all the pixels in the inspection area is taken independently, and the sum of the L value, a value, and b value is divided by the number of pixels to obtain the Lab color. The average L value, average a value, and average b value of the spatial histogram distribution are calculated (S145).

  The Lab value converted in Lab space is calculated according to Equation 4 below. The Lab color space is a type of complementary color space, and has a dimension L indicating lightness and complementary color dimensions A and B, and is based on nonlinearly compressed coordinates of the CIEXYZ color space. By converting from an XYZ value before normalization to Lab using Equation 4, a distribution in which the brightness direction is also taken into consideration in the Lab color space is obtained compared to the distribution in the XYZ color space.

In Equation 4, the values of X, Y, and Z in the parentheses of the function f are divided by the white point coordinates X _n , Y _n , and Z _n , respectively, in order to make the maximum value equal to 1.

  The difference between the average values of the reference object and the target object is taken as a material for judging the difference in color.

As shown in FIG. 7, the center coordinates C ₁ and C ₂ of the Lab color space histogram distribution are specified (S146). Here, the center coordinate is the centroid (center of gravity).

As shown in FIG. 7, the center coordinate deviation ΔF is set so that one of the two histogram distributions H ₁ (x, y) and H ₂ (x, y) coincides with the other center coordinates. The entire xy chromaticity distribution is shifted (mapped) (S147). This is because if one of the distributions is not shifted to the other distribution, the difference between the color components is also calculated. It can be done on the graph or just by calculation The shift amount can be set as appropriate. For example, the same effect can be obtained by shifting from one center to another center instead of shifting from one center to the other center. In short, it is only necessary to approach with an appropriate shift amount that allows the texture to be grasped.

  A material spread index indicating a spatial spread difference is calculated (S148). Thus, only the difference in metallic feeling is simply extracted, and the similarity of chromaticity and the degree of metallic feeling are separated and determined, and this can be quantified. The spread degree is calculated in the two-dimensional space of the xy chromaticity distribution, and the difference in the spread degree can be grasped as the difference in glittering feeling of the glittering material, excluding the color. It can be detected separately.

The material spread index is calculated by the following formula. The xy chromaticity histogram distribution is the cumulative number of pixels. FIG. 8D shows the overlapping region D, and FIG. 8E shows the minimum distribution.
Material spread index = total number of pixels belonging to overlapping region D / total number of pixels in inspection region K × 100 (%)

  The spread histogram of the reference object and the inspection object in the two-dimensional space is calculated, and the minimum value at the same position in the array is the overlap frequency. Therefore, this value is calculated by the total histogram total count. Calculated by dividing.

FIGS. 8D and 8E are cross-sectional views taken along the line SS of FIG. 8C, and there is an overlap when viewed on the same line in the xy coordinates. Instead of drawing in three dimensions, it is drawn in a plane for convenience. Moreover, since it is a histogram, it has a minute staircase distribution. 8 cumulative number _{H 1} and the accumulated number of _{H 2} (d) are respectively the image A, corresponding to the image B. When the two histogram distributions are compared, an overlap region D exists.

As shown in FIG. 8E, H ₁ (x ₁ , y ₁ ) is the cumulative number of xy chromaticity histogram distributions of the inspection object Q, and H ₁ (x ₂ , y ₂ ) is the xy chromaticity histogram of the inspection object R. When the accumulated number of distribution, in the overlapping left area _H 1> _{H 2,} centrally _H 1 = _{H 2,} and the on the right side is a _H 1 _{<H 2.} If the smaller integrated number (pixel frequency) of H ₁ and H ₂ is taken, it becomes H _{1 on} the left side and H ₂ on the right side, and a minimum distribution which is a step-like histogram curve can be specified. By using this, the ratio of the overlapping region D to the entire region can be calculated.

The smaller integrated value is specified by this minimum distribution. If the smaller integrated number of H ₁ and H ₂ is added and calculated, the integrated number of the overlapping region D can be calculated, and the ratio to the total number of pixels can be specified. The total number of pixels in the inspection region K is determined, and the total number of pixels of the inspection object Q and the reference object R is the same. The calculation of this ratio may be integrated three-dimensionally for all the lattices G. For example, as shown in FIG. 8C, the inspection region K is cut along the SS axis, and y is a predetermined value. The distribution of the cumulative number of pixels when x changes from end to end is two-dimensionally integrated. FIG. 8F is a two-dimensional map on the xy coordinates of the integration result. If there is no distribution in the inspection region K and the number of pixels is zero, it is excluded from the calculation.

  Finally, display / save processing and transmission processing are performed (S149), and the processing returns.

  For example, the pixels belonging to the inspection region K are assumed to be 100 vertical pixels × 100 horizontal pixels = 10,000 pixels. Since the image is cut out in the same inspection region K, the total number of pixels of the image A and the image B is 10,000 pixels. From the xy chromaticity histogram, the number of pixels in the overlapping area is integrated, and when the integration number is 5,000, the texture spread index is 50%. As the texture spread index falls below 100%, the degree of texture difference increases. If the distribution of the xy values is completely coincident, 100% is obtained. Thereby, when it determines with it being a numerical value more than fixed, it can determine with a texture being a conforming product.

  For the image, the color information obtained primarily is the three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) by a function equivalent to the XYZ color matching function, and is acquired by RGB. Compared to the case, it is more accurate and sensitive to the sensitivity of the human eye. Since the overlap of spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) is small, the S / N ratio is sufficient, and the curve in the spectral sensitivity curve changes naturally, so the error in colorimetry is Minimized.

  Since the texture of the image can be grasped from the histogram by separating it from the color, a subtle difference in color tone is reflected by reflecting the difference in the color texture (metallic feel, glitter, mottled pattern, color pattern, sensation, etc.) Can be determined.

  For example, as shown in FIGS. 9A to 9C, an example will be described in which three types are tested from a small metallic degree to a large metallic degree. An object having a small metallic degree is referred to as a reference object 1, an intermediate degree of metallic degree is referred to as an inspection object 2, and an object having a large metallic degree is referred to as an inspection object 3. First, when a distribution on the xyz chromaticity diagram after the above processing is performed for 1 to 3 is created, as shown in the xy chromaticity diagram of FIG. . The accumulated number is shown in light and dark. The brighter the color, the larger the accumulated number. FIG. 9C schematically shows the integrated number in three dimensions of the reference object and the inspection object. The xy axis is chromaticity, and the z axis is the cumulative number. Basically, the stronger the metallic sensation, the lower the Yamagata, and the weaker the metallic sensation, the sharper the Yamagata. The glittering material (aluminum flakes), which is the source of the metallic feeling, gives a glittering feeling when exposed to illumination light. This glittering feeling is physically a light diffraction phenomenon. By comparing the two histogram distributions of the reference object 1 and the inspection object 2 or 3, a texture spread index indicating the degree of overlap is calculated.

  In addition, as shown in Table 1, since the comparative example uses Lab in which ΔE is calculated based on the average color of the texture, the Lab value and ΔE value are very small compared to the appearance, and the inspection is It was difficult. As the texture spread index of the present embodiment, the cumulative number within the range of the inspection region K is used as it is, so that the inspection objects 2 and 3 are 58% and 27%, respectively, with respect to the reference object 1, which are clearly numerical values and , You can easily identify the metallic degree.

  Next, a color inspection apparatus 101 according to the second embodiment will be described with reference to FIGS. 10 and 11. For the corresponding similar elements, the explanation is cited as 100 series, and the difference is mainly explained.

  A camera 102 that captures an image of a reference object / inspection vehicle 105, a camera 102 connected to the camera 102 via a switch 106, an arithmetic processing device 103 that receives a signal and calculates a material spread index, and an arithmetic processor 103 connected to the arithmetic processing device 103 And a display device 107 that performs display.

  As illustrated in FIG. 10, the arithmetic processing device 103 captures an arithmetic unit 103 </ b> A that calculates a stimulus value XYZ <b> 1 acquired by imaging a color sample 105 that is a reference object R, and an image of a color sample that is an inspection object. The calculation unit 103B that calculates the acquired stimulus value XYZ2, the calculation unit 103A and the calculation unit 103B are connected, the calculation unit 103C that calculates the color matching index of the car, and the OK signal or the NG signal from the calculation unit 103C. It is transmitted to the display device 107 or transmitted to the outside. Note that the switch 106 selectively inputs the stimulus value XYZ1 and the stimulus value XYZ2. The target vehicle is an example.

FIG. 11A is a flowchart for calculating a texture spread index based on a comparison of XYZ color space histogram distributions from two images A and B. As shown in FIG. 11A, when the program is started, the inspection area K is cut out from the image A, specified, and set (S201). Next, an inspection area similar to that of the image A is cut out from the image B, specified, and set (S202). The chromaticity values XYZ are calculated from the images A and B (S203). In the inspection region K, the XYZ color space histogram distributions of the inspection object Q and the reference object R are calculated and created (S204). The minimum distribution of the XYZ color space histogram distribution is specified (S205). The calculation of the average XYZ value of the inspection area of image A and the calculation of the average XYZ value of the inspection area of image B are performed. You may replace with the calculation of a Lab value. The integrated number of the XYZ color space histogram distribution in the overlapping region D is calculated (S206). The material spread index is calculated (S207) and the process returns. Texture spread index = (the integrated number of pixels belonging to the overlapping region D / the total number of pixels in the inspection region K) × 100 (%). As shown in FIG. 12, the accumulated number in the overlapping area D of T ₁ and T _2, adds calculates the cumulative number of lesser.

In the case of the calculation of the XYZ distribution corresponding to the inspection region K, the calculation of the exponent is performed by the distribution in the three-dimensional space of the X axis, the Y axis and the Z axis. As shown in FIGS. 12A and 12B, the XYZ values of the inspection object Q and the reference object R in the XYZ space coordinates are T ₁ (L, a, b) and T ₂ (L, a, b), respectively. ). The XYZ color space and the histogram distribution are shaped like a globe, and there are cases where the two histogram distributions are three-dimensionally overlapped and separated. The inspection area K in the three-dimensional space is partitioned by a lattice, and the color space histogram distribution and minimum distribution of T ₁ (X, Y, Z) and T ₂ (X, Y, Z) in three dimensions are obtained, and similar indices are obtained. Perform the operation. The accumulated number of grids is projected on a plane, and the accumulated number of overlapping areas on the grid is calculated by the same accumulation within the plane. In the case of XYZ chromaticity, there is no brightness information, and therefore the XYZ space does not change even if the brightness of the image changes.

  When the Lab color space histogram is used for texture determination instead of the XYZ color space histogram, the flowchart of FIG. 11B is used. The description of FIG. 11B uses the above description of FIG. 11A. In S205, the average Lab value of the area and the average Lab value of the inspection area of the image B are calculated.

  Next, the coloring inspection apparatus 201 of Embodiment 3 will be described with reference to FIG. About the corresponding similar element, description is used as 200 series, and a difference is mainly demonstrated.

  As illustrated in FIG. 13, the color determination target is a partial region of the color sample 5, and the camera 202 captures an image of the target region of the color sample 5. The arithmetic processing unit 203 is connected to the arithmetic unit 203A that calculates Lab from the reference stimulus value XYZ1, the arithmetic unit 203B that calculates Lab from the stimulus value XYZ2 to be determined, the arithmetic unit 203A, and the arithmetic unit 203B. The calculation unit 203C that calculates the average value, the texture spread index calculation unit 203D that calculates the texture spread index from the reference Lab and the target Lab, and the calculation values from the calculation units 203C and 203D are transmitted to the coloring device 257. According to the index value, whether or not the paint color is appropriate is determined by looking at the screen and further colored. Note that the switch 206 selectively inputs the reference XYZ and the target XYZ. Since the main processing is generally the same as the flowcharts of the first and second embodiments, the description is incorporated.

In the case of calculation of the chromaticity histogram distribution in the Lab space corresponding to the inspection area K, the XYZ value is converted to Lab. The exponent is calculated by the distribution in the three-dimensional space of the L axis, a axis, and b axis. The Lab color space histogram distribution is a solid elliptical shape. The Lab values in the Lab space coordinates of the inspection object Q and the reference object R are respectively U ₁ (L, a, b) and U ₂ (L, a, b) as shown in FIGS. ). In the Lab color space, the histogram distribution has a globe-like shape, and the two histogram distributions may be three-dimensionally overlapped or separated. The inspection area K in the three-dimensional space is partitioned by a grid, and the chromaticity histogram distribution and minimum distribution of U ₁ (L, a, b) and U ₂ (L, a, b) in three dimensions are obtained, and similar indices are obtained. Perform the operation. The accumulated number of grids is projected on a plane, and the accumulated number of overlapping areas on the grid is calculated by the same accumulation within the plane. In the case of Lab chromaticity, since there is brightness information, in the Lab space, when the brightness of the image changes, the L value changes, and the coincidence distributions U ₁ and U ₂ are positioned in the Lab space. Therefore, it is possible to make a determination taking light and dark into account. This is because the position of the distribution is shifted if the brightness of the image is different. For example, the Lab color space histogram distribution shifts downward when dark and shifts upward when bright.

  Furthermore, the processing by the color inspection apparatus according to the fourth embodiment of the present invention is an example in which skin gloss and shine in the Lab color space histogram distribution are determined based on the difference in distribution, as shown in FIG.

  In the case of the xy chromaticity histogram distribution, the color is determined by ΔL, Δa, Δb values and ΔE (√ (ΔL ** 2 + Δa ** 2 + Δb ** 2) (** 2 means square). The texture spread index of only the color spread distribution is converted into a numerical value in the two-dimensional distribution of normalized chromaticity values x and y not taking into account the L value, and in the case of the XYZ color space histogram distribution, each X value of XYZ, The color is determined based on the average value of the Y value and the Z value (color difference determination using the Lab value is also OK), and the texture is determined based on the texture spread index based only on the distribution of the XYZ color space histogram. , Δa, Δb values, and ΔE (√ (ΔL ** 2 + Δa ** 2 + Δb ** 2). For the texture, the texture is determined by the texture spread index based only on the distribution of the Lab color space histogram.

  Other application examples will be described. Two images A and B acquired for the reference product and the inspection product are overlapped, and each chromaticity histogram distribution or color space histogram is displayed on the display device 7, or each chromaticity histogram distribution or color space is displayed. A chromaticity diagram obtained by superimposing histograms on one chromaticity diagram can be displayed, and a color difference can be determined by an average Lab value. On the other hand, a material spread index calculation showing a metallic feeling can be separated and displayed as a percentage. As a result, the deviation of the texture from the reference product of the chromaticity distribution of the inspection product, in particular, the metallic feeling can be reliably confirmed numerically. The inspection results are displayed numerically for each region K. The grid width of the lattice can be adjusted. An exponent threshold value can be arbitrarily set. Measurement results and captured images can be saved. It is possible to perform standardization of colors and stable color management by reducing problems of individual differences that cannot be avoided by visual inspection and troubles in judgment standards with customers.

  Since non-contact and wide-area shooting is possible, it is possible to digitize flip-flops by capturing the color sample 5 from a plurality of angles with flat illumination, such as painting with aluminum flakes or pearl pigments. Evaluation that is close to the color and texture of the eyes is possible. Irregular pattern parts such as wood grain panels can also be matched. Since the captured images A and B can be displayed on the display device 7 (overlay function), alignment can be performed easily. The inspection object can be compared with the reference object even if the size and material are different. Colors can also be used for fabrics with irregular patterns and textures such as leather. Resin parts inspection, color unevenness and color misregistration inspection are possible. For example, even an object with unevenness can be measured. Colors can also be used for building materials with irregular patterns and textures such as flooring, irregular patterns such as wallpaper, and textures such as woodgrain, marble and geometric patterns. You can inspect the texture of teeth in the dental field.

Although the present embodiment has been described above, the following effects are obtained. (1) Average L value, Average a value, Average b value, and (2) Two H ₁ (x, y), H ₂ (x, y), T ₁ (X, Y, Z), T ₂ Examples of the material spread indexes relating to (X, Y, Z), U ₁ (L, a, b), U ₂ (L, a, b) have been given. By presenting the difference in color separately, the color difference can be adjusted more appropriately by adjusting the color of the paint that composes it, and the count adjustment of the glitter material that produces a metallic feeling In addition, it is possible to give an appropriate policy for adjusting the texture of glazes such as ceramics and adjusting the texture of the metal surface. As the metallic feeling becomes stronger, the xy chromaticity histogram, the XYZ color space histogram, or the Lab color histogram distribution becomes wider. As a difference from the chromaticity histogram, the XYZ color space histogram, or the Lab color histogram distribution, a guideline can be presented as to how much difference there is. Color matching work between the actual paint color and the original standard color becomes very easy.

  The embodiments of the present invention are not limited to the above-described embodiments, and modifications and the like can be made without departing from the technical idea of the present invention. Needless to say, objects and the like are also included in the technical scope of the present invention and can take various forms as long as they belong to the technical scope. For example, with respect to a method of acquiring image information according to three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)), the methods given in the present embodiment are only specific examples. However, the technical idea of the present invention is not limited to the above and can be implemented by other methods.

The color inspection apparatus according to the present invention accurately grasps the metallic feeling caused by the glitter material placed on the paint, which cannot be grasped by a comprehensive index, and supports color matching work.
Applicable to.

DESCRIPTION OF SYMBOLS 1, 101, 201 ... Coloring inspection apparatus 2, 102, 202 ... Camera 3, 103, 203 ... Arithmetic processing unit 5, 105, 205 ... Color sample 6, 106, 206 ... Illumination Unit 7: Display device 21: Shooting lenses 22a, 22b, 22c ... Optical filter 23 ... Imaging elements 22a ', 22c' ... Dichroic mirrors 23a, 23b, 23c ... Imaging elements

As shown in FIG. 7, the center coordinates C ₁ and C ₂ of the xy color space histogram distribution are specified (S146). Here, the center coordinate is the centroid (center of gravity).

FIG. 11A is a flowchart for calculating a texture spread index based on a comparison of XYZ color space histogram distributions from two images A and B. As shown in FIG. 11A, when the program is started, the inspection area K is cut out from the image A, specified, and set (S201). Next, an inspection area similar to that of the image A is cut out from the image B, specified, and set (S202). The chromaticity values XYZ are calculated from the images A and B (S203). In the inspection region K, the XYZ color space histogram distributions of the inspection object Q and the reference object R are calculated and created (S204). The calculation of the average XYZ value of the inspection area of the image A and the calculation of the average XYZ value of the inspection area of the image B are performed (S205) . It may be replaced with the calculation of the Lab value as shown in FIG. 11B . The center coordinates of the XYZ color space distribution are specified (S206). The center coordinates of the XYZ color space distribution to the center coordinates are specified (S206). Shift processing of the XYZ color space distribution to the center coordinates is performed (S207). The center coordinates of the XYZ color space histogram distribution are specified in the three-dimensional space (XYZ space) (S208). The minimum distribution of the color space histogram distribution is specified, and the integrated number of the XYZ color space histogram distribution in the overlapping region D is calculated (S20 9 ). The material spread index is calculated (S2 10 ) and the process returns. Texture spread index = (the integrated number of pixels belonging to the overlapping region D / the total number of pixels in the inspection region K) × 100 (%) . Cumulative number in an overlapping area D of T ₁ and T _2, adds calculates the cumulative number of lesser.

As shown in FIG. 1, the camera 2 is disposed behind the photographing lens 21, the three optical filters 22a, 22b, and 22c disposed behind the photographing lens 21, and the optical filters 22a, 22b, and 22c. An image sensor 23 (CCD, CMOS, etc.). The three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) of the camera 2 are given by the product of the spectral transmittance of the optical filters 22a, 22b, and 22c and the spectral sensitivity of the image sensor 23. It is. The arrangement relationship between the optical filters 22a, 22b, and 22c and the image sensor 23 in FIG. 1 is merely shown schematically. Specific examples of the method of acquiring image information according to the three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) will be given below. In the first embodiment, any of these may be adopted. It is also possible to adopt other methods. Reference numeral 24 denotes a calculation unit, and 25 denotes a display unit.

FIG. 3A shows a system using a dichroic mirror. This is because light of a specific wavelength is reflected by the dichroic mirror 22c ′, and the remaining light that has been transmitted is further reflected by another dichroic mirror 22a ′ to be spectrally separated, and the image pickup devices 23a, 23b. , 23c are read in parallel. Here, the dichroic mirror 22a ′ corresponds to the optical filters 22a and 22b, and the dichroic mirror 22c ′ corresponds to the optical filter 22c. Light incident from the photographic lens 21 is reflected by the dichroic mirror 22c ′ according to the spectral sensitivity S3, and the remaining light is transmitted. The light reflected by the dichroic mirror 22c ′ is reflected by the reflecting mirror 26, and the spectral sensitivity S3 is obtained by the imaging device 23c. On the other hand, the light transmitted through the dichroic mirror 22c ′ is reflected by the dichroic mirror 22a ′, and the light according to the spectral sensitivity S1 is transmitted, and the remaining light according to the spectral sensitivity S2 is transmitted . The light transmitted through the dichroic mirror 22a ′ is imaged by the image sensor 23b to obtain the spectral sensitivity S2. The light reflected by the dichroic mirror 22a ′ is reflected by the reflecting mirror 29, and the spectral sensitivity S1 is obtained by the imaging device 23a . Instead of the dichroic mirror, a dichroic prism having the same characteristics may be used to split the light into three, and the image sensors 23a, 23b, and 23c may be bonded to the positions where each light is transmitted.

Claims (2)

A camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalent to the CIE XYZ color matching function;
An arithmetic processing unit that acquires and calculates coloring data obtained by converting an image having three spectral sensitivities acquired by the camera into tristimulus values X, Y, and Z in the CIE XYZ color system;
An illumination unit for irradiating the measurement object;
With
The arithmetic processing unit is
Of the coloring data obtained by imaging the measurement object, set the specified inspection area,
An inspection area having coordinates corresponding to the color space of the XYZ color system is partitioned by a grid, and the number of pixels of the test object and the reference object belonging to each grid is integrated to obtain a color space histogram distribution of the XYZ color system. make,
By specifying the centers of the two color space histogram distributions of the inspection object and the reference object and shifting the center of one of the color space histogram distributions so as to be close to the center of the other color space histogram distribution, A coloring inspection apparatus that calculates a texture spread index indicating a difference in spread of a histogram distribution. In a color inspection method using a camera having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to a CIE XYZ color matching function,
Generating colored data obtained by converting an image having three spectral sensitivities acquired by imaging by the camera under illumination into tristimulus values X, Y, and Z in the CIE XYZ color system;
Of the coloring data obtained by imaging the measurement object, setting the specified inspection region,
A step of creating a color space histogram distribution by dividing an inspection area of coordinates corresponding to each color space of the XYZ color system by a grid and integrating the number of pixels of the inspection object and the reference object belonging to each grid; ,
The center of the two color space histogram distributions of the inspection object and the reference object is specified, and the center of one of the color space histogram distributions is shifted so as to be close to the center of the other color space histogram distribution, and the color space histogram Calculating a material spread index indicating a difference in distribution spread;
A coloring inspection method comprising: