JP2018115877A - Surface color and texture management system and management method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To appropriately utilize nonstandard products which are not compliant with standards, to suitably perform the matching of color and texture, to reduce production cost, and to achieve effective use of resources.SOLUTION: A management server 6 includes: a product distribution calculation unit 9 for storing coordinates corresponding to a color space of an XYZ calorimetric system from three band visual sensitivity images S1i, S2i and S3i of a product W, partitioning the coordinates with lattices, accumulating the number of pixels of an inspection plane to which each lattice belongs, creating color space histogram distribution of the XYZ calorimetric system and storing the color space histogram distribution; and a texture distribution index computing unit 10 for comparing the distribution of nonstandard products with that of the product W to calculate a texture distribution index.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a surface color / texture management system and management method for managing surface color / texture data on the Internet.

  According to the coloring evaluation apparatus and the coloring evaluation method of Patent Document 1, it has been proposed to clearly and easily quantify the metallic feeling and the texture such as pearl glitter when assembling different materials, thereby rationalizing the painting process.

  Manufacture part A of an industrial product at parts factory 1 and paint it with the same color, manufacture part B of an industrial product at part factory 2 and paint these parts with the same color. In particular, an IC tag storing unique information, for example, part number, manufacturing factory name, manufacturing date and time, etc. is attached by built-in or mounting, etc., and coloring evaluation apparatus for parts A and B after painting at the parts factory Color information such as color distribution is measured and stored, and the color information is stored in the IC tag in association with the part number. The serial number and color information data are matched by a computer to create a table of component combinations. Reads information from IC tags, flows painted parts to the line, and assembles industrial products with the optimal combination of parts based on the table.

Japanese Patent Laid-Open No. 2006-161297

  However, parts that fail the color matching inspection are basically discarded.

  For this reason, for example, in the condominium industry, there is a need for a part of tiles affixed to the wall of an apartment kitchen or an outer wall to be cracked, damaged, fouled, or replaced. Even if it is replaced, the color will not match the surrounding tiles that have changed color due to deterioration over time. Tile is a pottery, making custom tiles is a very difficult task, and the condominium industry is struggling with countermeasures. In the automobile industry, even if it is a new model vehicle, if the surface becomes tanned due to use and the bumper that is a part is damaged, when you want to replace it with a new bumper later, the painted surface and texture of the car will be miscolored. Because the texture is shifted, even if a bumper similar to the shifted painted surface is required, a genuine bumper may not fit. In fact, it would be expensive to produce a newly colored custom made bumper. Similar problems occur in the electrical industry, the pharmaceutical industry, and the like.

  Therefore, an object of the present invention is to use a non-standard product that is incompatible with the standard, perform an appropriate color / texture matching, reduce manufacturing costs, and achieve effective use of resources. .

As shown in FIG. 1, the first invention of the present invention has three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) that are linearly converted equivalently to the CIE XYZ color matching functions. A storage unit for acquiring and storing three-band visual sensitivity images S1i, S2i, S3i having three spectral sensitivities from a non-standard product by an imaging unit;
The coordinates corresponding to the color space of the XYZ color system are stored from the non-standard three-band visual sensitivity images S1i, S2i, S3i, the coordinates are partitioned by grids, and the number of pixels of the inspection surface belonging to each grid is determined. A non-standard product distribution calculation unit that creates a color space histogram distribution of the XYZ color system by integrating, and stores the color space histogram distribution and the product-specific information corresponding to the database in association with each other;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, and S3i are acquired, coordinates corresponding to the color space of the XYZ color system are stored from the three-band visual sensitivity images S1i, S2i, and S3i of the above-described product, and the coordinates are partitioned by a grid. Then, by integrating the number of pixels of the inspection surface belonging to each grid, to create a color space histogram distribution of the XYZ color system and store the color space histogram distribution,
A texture distribution index calculation unit that compares and determines the distribution of the non-standard product and the distribution of the implementation product and calculates a texture distribution index;
An output unit that refers to the texture distribution index and identifies and outputs the product-specific information of a non-standard product that approximates the color / texture to the implementation product,
A color / texture management system, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet.

  The “non-standard product” here refers to a product that does not satisfy the standard as a result of inspection by visual inspection, a sensor, an inspection device, or the like. An “implemented product” is a product that is manufactured, meets the standards, is distributed in the market, and is used.

  The “imaging unit” may be singular or plural. The imaging unit may be fixed or imaged, or moved and imaged. When there are a plurality of imaging units, they can be installed at locations corresponding to imaging angles. An XYZ two-dimensional colorimeter is exemplified.

  The image pickup unit in the present invention preferably picks up an image of an observation target by dividing it into three channels by means of three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)). Can be used regardless of whether it is an optical filter, a dichroic mirror, a dichroic prism, or the like set to obtain these spectral sensitivities.

  The spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) of the “imaging unit” is a mountain shape having a single peak that has no negative value from the CIE XYZ spectral characteristics, and each spectral sensitivity curve. Are equivalently converted under the condition that the peak values of the spectral characteristics are equal and the overlap of spectral sensitivity curves is minimized. The curve of the spectral characteristic S1 has a peak wavelength of 582 nm and a half-value width of 523 to 629 nm. 1/10 width is 491 to 663 nm. The curve of the spectral characteristic S2 has a peak wavelength of 543 nm, a full width at half maximum of 506 to 589 nm, and a 1/10 width of 464 to 632 nm. The curve of the spectral characteristic S3 has a peak wavelength of 446 nm, a full width at half maximum of 423 to 478 nm, and a 1/10 width of 409 to 508 nm.

  The two-dimensional colorimeter adopts a multi-point simultaneous measurement method using an image sensor, and preferably enables color / texture evaluation, and can be used in the automobile industry, the home appliance industry, the cosmetics industry, the printing industry, the building materials industry, and the like.

  The “color space” is a color that can be designated as the coordinates of one point in the space when the size that can be designated by three independent quantities is represented by the distance from the origin in the three axial directions.

  “XYZ color system” means a broad meaning including other CIE color systems, and in the embodiment means a narrow meaning. 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 a 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 ^*, which is derived from the XYZ color system in order to measure the color difference ΔE due to the difference between perception and device. In Japan, it is specified in JIS Z 8729.

  The “XYZ color system” 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 distribution 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.

  For the mode of “shifting closer”, for example, a predetermined position of the histogram distribution, for example, the centers are matched, a predetermined position, for example, the centers are moved close to each other within a predetermined range, and moved in parallel with the coordinate axis. The shift amount, the shift direction, etc. can be set as appropriate as long as an appropriate index can be obtained, such as close proximity or proximity from the center to another center. It is.

  By dividing the coordinates corresponding to the color space of the XYZ color system based on the three-band visual sensitivity images S1i, S2i, S3i with grids, and integrating the number of pixels of the non-standard product and the implemented product belonging to each grid, Create a color space histogram distribution of the XYZ color system, identify the centers of the two color space histogram distributions of the non-standard product and the implementation product, and use one of the color space histogram distributions as the other color space histogram distribution It is preferable to calculate a texture distribution index indicating a difference in the spread of the color space histogram distribution by shifting so as to be close to the center of the color space.

  When the color temperature that is the imaging environment of the first imaging unit and the second imaging unit of the present invention is different, it is preferable to correct the spectral sensitivity with the color temperature. With a two-dimensional colorimeter having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalent to the CIE XYZ color matching functions, a plurality of products can be obtained under a standard light source. A storage step for storing a plurality of captured three-band visual sensitivity images S1i, S2i, S3i, a color temperature measurement unit that measures a color temperature with a color temperature measurement sensor under remote illumination light, and the three-band visual sensitivity image S1i , S2i, and S3i are preferably provided with an illumination color conversion correction unit that converts the color temperature correction image into a corrected image based on the measurement value of the color temperature measurement sensor.

  Examples of the two-dimensional colorimeter include an XYZ colorimeter, a wearable glass having a display unit in glasses, or an XYZ wearable device having a head-mounted display.

  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.

The second invention of the present invention is a non-standard by the first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function. Three-band visual sensitivity images S1i, S2i, S3i having three spectral sensitivities are obtained from the product, and the three-band visual sensitivity images S1i, S2i, S3i are obtained from the non-standard three-band visual sensitivity images S1i, S2i, S3i. A storage unit for storing the XYZ values of the respective pixels or the xy values normalized from the XYZ values;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution calculation unit that stores a degree histogram three-dimensional distribution or a Lab chromaticity histogram three-dimensional distribution in the database of the management server in association with the product-specific information corresponding to the color space histogram distribution;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, S3i are acquired, and the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram are partitioned by a grid, and the number of pixels of the nonstandard product belonging to each grid is integrated. An actual product distribution calculation unit that calculates the xy chromaticity histogram distribution, the XYZ chromaticity histogram three-dimensional distribution, or the Lab chromaticity histogram three-dimensional distribution created by
A texture distribution index calculation unit that compares and determines the distribution of the non-standard product and the distribution of the implementation product and calculates a texture distribution index;
An output unit that refers to the texture distribution index and identifies and outputs the product-specific information of a non-standard product that approximates the color / texture to the implementation product,
A color / texture management system that performs data communication of the storage unit, nonstandard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit via the Internet.

A third invention of the present invention comprises a management server having a database and managing color and texture;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from non-standard products. A first client that stores the three-band visual sensitivity images S1i, S2i, S3i and transmits them to the management server via the Internet;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from the product 3 A second client for storing the band visual sensitivity images S1i, S2i, S3i and transmitting the images to the management server via the Internet;
A management server for receiving three-band visual sensitivity images S1i, S2i, S3i from the first and second clients via the Internet and storing them in a database;
With
The non-standard product and the three-band visual sensitivity images S1i, S2i, and S3i of the product are normalized by the XYZ values of the respective pixels of the three-band visual sensitivity images S1i, S2i, and S3i, or the XYZ values, respectively. Store the xy value,
An xy chromaticity histogram distribution created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the non-standard product and the implemented product belonging to each grid. , XYZ chromaticity histogram three-dimensional distribution, or Lab chromaticity histogram three-dimensional distribution is stored in the database of the management server,
For the distribution of the non-standard product, store the color space histogram distribution and the corresponding product specific information in association with each other,
Compare and determine the distribution of the non-standard product and the distribution related to the implementation product, calculate a texture distribution index,
With reference to the index, the product-specific information of a non-standard product whose color / texture approximates the implementation product is output from the first client.
It is preferable.

  Instead of calculating the distribution at the management server, the first client and the second client may use the three-band visual sensitivity images S1i, S2i, S3i and the distribution calculated from the images as Internet It is preferable to transmit the data to the management server via

  Preferably, a tag is attached to the non-standard product, and the distribution of the non-standard product and the product-specific information are stored in the tag.

  As the “tag”, an appropriate tag such as an IC tag, a barcode, a QR code (registered trademark), or the like can be selected.

  The distribution belonging to the non-standard product stored in the tag and read out is compared with the distribution related to the implementation product, and the product-specific information of the non-standard product whose color / texture approximates the implementation product is determined. It is preferable to send to two clients.

The first client is
A color temperature measuring unit for measuring the color temperature by the first color temperature measuring sensor;
The second client is
A color temperature measuring unit for measuring the color temperature by the second color temperature measuring sensor;
Illumination that converts the three-band visual sensitivity images S1i, S2i, and S3i of the non-standard product and the practical product into corrected images that have undergone color temperature correction based on the measurement values of the first and second color temperature measurement sensors. A color conversion correction unit;
It is preferable to have provided.

According to a fourth aspect of the present invention, the first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function is used. A storage step of acquiring and storing three-band visual sensitivity images S1i, S2i, S3i having three spectral sensitivities from the product;
The coordinates corresponding to the color space of the XYZ color system are stored from the non-standard three-band visual sensitivity images S1i, S2i, S3i, the coordinates are partitioned by grids, and the number of pixels of the inspection surface belonging to each grid is determined. A non-standard product distribution calculation step of creating a color space histogram distribution of the XYZ color system by integrating and storing the color space histogram distribution and product-specific information corresponding to the database in association with each other;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, and S3i are acquired, coordinates corresponding to the color space of the XYZ color system are stored from the three-band visual sensitivity images S1i, S2i, and S3i of the above-described product, and the coordinates are partitioned by a grid. Then, by integrating the number of pixels of the inspection surface belonging to each grid, to create a color space histogram distribution of the XYZ color system, and to store the color space histogram distribution, an implementation product distribution calculation step,
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
An output step of identifying and outputting the product-specific information of a non-standard product whose color / texture approximates the implemented product with reference to the texture distribution index,
A color / texture management method, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet.

According to a fifth aspect of the present invention, the first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function is used. Three-band visual sensitivity images S1i, S2i, S3i having three spectral sensitivities are obtained from the product, and the three-band visual sensitivity images S1i, S2i, S3i are obtained from the non-standard three-band visual sensitivity images S1i, S2i, S3i. A storage step of storing the XYZ value of each of the pixels or the xy value normalized from the XYZ value;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution calculation step of storing a color histogram histogram three-dimensional distribution or a Lab chromaticity histogram three-dimensional distribution in the management server database in association with the product-specific information corresponding to the color space histogram distribution;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, S3i are acquired, and the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram are partitioned by a grid, and the number of pixels of the nonstandard product belonging to each grid is integrated. A product distribution calculation step for calculating the xy chromaticity histogram distribution, the XYZ chromaticity histogram three-dimensional distribution, or the Lab chromaticity histogram three-dimensional distribution created by
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
An output step of identifying and outputting the product-specific information of a non-standard product whose color / texture approximates the implemented product with reference to the texture distribution index,
A color / texture management system comprising: storing, non-standard product distribution calculation, implementation product distribution calculation, color / texture calculation, and data transmission / reception of an output unit via the Internet.

According to a sixth aspect of the present invention, there is provided an imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function from a nonstandard product. A first transmission step of storing the acquired three-band visual sensitivity images S1i, S2i, S3i having the three spectral sensitivities and transmitting them from the first client to the management server via the Internet;
From the non-standard three-band visual sensitivity images S1i, S2i, S3i, the XYZ values of the pixels of the three-band visual sensitivity images S1i, S2i, S3i, or the xy values normalized from the XYZ values are stored. A memory step;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution storage step of storing the color space histogram distribution and the product specific information corresponding to the color space histogram distribution in the database of the management server;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from the product 3 A second transmission step of storing the band visual sensitivity images S1i, S2i, S3i, and transmitting them from the second client to the management server via the Internet;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid An implementation product storage step of storing the degree histogram three-dimensional distribution or the Lab chromaticity histogram three-dimensional distribution in the database of the management server;
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
Referring to the texture distribution index, specifying the product-specific information of a non-standard product whose color / texture approximates the implemented product, and outputting from the first client;
A color / texture management method characterized by comprising:

  Instead of calculating the distribution at the management server, the first client and the second client may use the three-band visual sensitivity images S1i, S2i, S3i and the distribution calculated from the images as Internet And transmitting the data to the management server via the network.

The non-standard product storage step includes:
A tag information storing step of attaching a tag to the non-standard product, and storing the distribution of the non-standard product and the product-specific information in the tag;
The output step comprises:
A tag output step of comparing and determining the distribution of non-standard products stored in the tag and the distribution of the implementation products, and outputting product-specific information of the non-standard products whose color / texture approximates the implementation products; Are preferably provided.

The first transmission step includes
A color temperature measurement step of measuring the color temperature by the first color temperature measurement sensor;
The second transmission step includes
A color temperature measuring step of measuring a color temperature by a second color temperature measuring sensor;
Illumination that converts the three-band visual sensitivity images S1i, S2i, and S3i of the non-standard product and the practical product into corrected images that have undergone color temperature correction based on the measurement values of the first and second color temperature measurement sensors. A color conversion correction step;
It is preferable to have provided.

  The present invention makes it possible to use a non-standard product that does not conform to the standard, perform an appropriate color / texture matching, reduce the manufacturing cost, and achieve effective use of resources.

It is a block diagram of the color / texture management system of the present invention. 1 is a block diagram of a color / texture management system 1 according to Embodiment 1 of the present invention. FIG. It is explanatory drawing of the color / texture management system for houses which applied the color / texture management system 1 of Embodiment 1 of this invention. It is a connection block diagram of the two-dimensional colorimeter of Embodiment 1 of the present invention. It is a function which shows the spectral sensitivity of the two-dimensional colorimeter 2 which is an XYZ color system camera in Embodiment 1 of this invention. This is a specific example of a method for acquiring an image 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 block diagram of the management server 6 of the color and texture management system 1 of Embodiment 1 of this invention. It is a flowchart in the two-dimensional color meter 2 of Embodiment 1 of this invention. It is a flowchart in the calculating part 62 of this invention Embodiment 1. FIG. It is a sub chart in the calculating part 62 of this invention Embodiment 1. FIG. It is explanatory drawing which shows the shift process in xy coordinate space in the calculating part 62 of this invention Embodiment 1. FIG. (A) is explanatory drawing which shows the test | inspection area | region T in the calculating part 62 of invention Embodiment 1, (b) is xy chromaticity diagram which shows the test | inspection area K on the chromaticity diagram corresponding to the test | inspection area T, (c) is. An explanatory diagram of the inspection region K partitioned by the grid G, (d) is a schematic diagram showing the state of chromaticity overlapping on the xy two-dimensional chromaticity diagram, (e) is an explanatory diagram showing a minimum distribution, (f) FIG. 6 is an explanatory diagram illustrating an example of an xy chromaticity histogram distribution. (A) is explanatory drawing which shows the roughness of the surface, (b) is an xy chromaticity histogram distribution diagram, (c) is a three-dimensional image diagram of the xy chromaticity histogram distribution. It is a flowchart (XYZ color space distribution) in the calculating part 62 of the color and texture management system of Embodiment 2 of this invention. It is a flowchart (Lab color space distribution) in the calculating part 62 of the color and texture management system of Embodiment 2 of this invention. It is explanatory drawing which shows the shift process in xy coordinate space of the color and texture management system of Embodiment 3 of this invention. It is explanatory drawing which shows the shift process in the Lab coordinate space of the color and texture management system of Embodiment 3 of this invention. It is explanatory drawing 1 of the color and texture management system of Embodiment 4 of this invention. It is explanatory drawing 2 of the color and texture management system of Embodiment 4 of this invention. It is a block diagram in the case of performing the color correction by the micro optical system of the color / texture management system of Embodiment 5 of the present invention. It is a flowchart in the management server of the color and texture management system of Embodiment 6 of this invention. It is a flowchart of a process of the illumination color conversion correction | amendment part of Embodiment 6 of this invention. It is explanatory drawing of the process of the illumination color conversion correction | amendment part of Embodiment 6 of this invention. It is explanatory drawing of the process of the illumination color conversion correction | amendment part of Embodiment 6 of this invention. It is a block diagram of the color and texture management system 1 of Embodiment 7 of this invention. It is explanatory drawing of the test result of this invention Example 1. FIG. It is explanatory drawing of the test result of this invention Example 2. FIG.

  The color / texture management system 1 according to the preferred embodiment 1 of the present invention is an example applied to the color / texture management of the tile T of the house J, and will be described with reference to FIGS.

  As shown in FIG. 2, the color / texture management system 1 is a two-dimensional image having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) that are linearly converted equivalent to the IE XYZ color matching functions. The colorimeter 2 has a storage unit 3 for acquiring and storing three-band visual sensitivity images S1i, S2i, and S3i (hereinafter abbreviated as image A) having three spectral sensitivities from a nonstandard product NG. The first client 4 which is a computer for transmitting the image to the management server 6 is provided.

  The color / texture management system 1 divides the coordinates for storing the coordinates corresponding to the color space of the XYZ color system from the three-band visual sensitivity images S1i, S2i, S3i of the non-standard product NG with each grid. By adding up the number of pixels of the inspection surface to which it belongs, a color space histogram distribution of the XYZ color system is created and stored in the database DB in association with the product unique information ID corresponding to the color space histogram distribution A management server 6 having a product distribution calculation unit 5 is provided.

  By the first client 4, the tag 15 also has a non-standard NG three-band visual sensitivity image S 1 i, S 2 i, S 3 i (XYZ values may be used) and / or a created color space histogram distribution and / or correspondence The product unique information ID to be stored is stored.

  The non-standard product NG here is manufactured at a manufacturing factory M of a tile maker, and is rejected as a result of inspection as to whether it conforms to the standard in the inspection process.

  The two-dimensional colorimeter 7 having three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function is used to obtain three spectral sensitivities from the product W. The three-band visual sensitivity images S1i, S2i, and S3i are acquired and stored in the second client 8, and the second client 8 transmits the three-band visual sensitivity images S1i, S2i, and S3i to the management server 6.

  The management server 6 stores coordinates corresponding to the color space of the XYZ color system from the three-band visual sensitivity images S1i, S2i, S3i (hereinafter, the image may be abbreviated as B) of the implementation product W. An actual product distribution calculation unit 9 that creates a color space histogram distribution of the XYZ color system by storing the coordinates by dividing the coordinates with a grid and integrating the number of pixels of the inspection surface belonging to each grid, and stores the color space histogram distribution. Have

  The management server 6 includes a texture distribution index calculation unit 10 that compares and determines the distribution of the non-standard product and the distribution of the implementation product W and calculates a texture distribution index.

  The color / texture management system 1 is a computer having an output unit 11 that identifies the product-specific information ID of a nonstandard product NG whose color / texture approximates the implementation product W with reference to the texture distribution index. 3 clients 12 are provided. The third client 12 is owned by a distribution warehouse company.

  The color / texture management system 1 is characterized in that data communication of the first client 4, the management server 6, the second client 8, and the third client 12 is performed via the Internet I.

The spectral sensitivity of the two-dimensional colorimeter 2 satisfies the router condition, and the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are obtained from XYZ color matching functions as shown in FIG. This is a mountain shape having no negative value and having 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 two-dimensional color meter 2 are, for example, the two-dimensional color meter RC-500 of the limited company Paparabo, effective frequency value of about 5 million pixels, effective area 9.93mm × 8.7mm, image size 3.45μm × 3.45μm, video Output 12Bit, camera interface GigE, number of frames (during focus adjustment) 3 to 7 frames / Sec, shutter speed 1 / 15,600Sec to 1 / 15Sec, integration time up to 3 seconds, S / N ratio 60dB or more, lens mount F mount, The operating temperature is 0 ° C to 40 ° C and the operating humidity is 20% to 80%.

  As shown in FIG. 4, the two-dimensional colorimeter 2 has a photographing lens 21, three optical filters 22a, 22b, and 22c arranged behind the photographing lens 21, and a rear of the optical filters 22a, 22b, and 22c. And an image pickup device 23 (CCD, CMOS, etc.) arranged. The three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) of the two-dimensional colorimeter 2 are obtained by multiplying the spectral transmittances of the optical filters 22a, 22b, 22c and the spectral sensitivity of the image sensor 23. Is given. The arrangement relationship between the optical filters 22a, 22b, and 22c and the image sensor 23 in FIG. 4 is merely schematically shown. Specific examples of methods for acquiring images according to the three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) will be given below. In the first embodiment, any of these can be adopted. Also, other methods can be adopted.

  FIG. 6A 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. Therefore, the light is captured by the image sensor 23a and the image sensor 23b, respectively. 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.

  FIG. 6B shows a system using 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. 6C 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 management server 6 communicates with the first client 4, the second client 8, and the third client 12 via the Internet. Basically, the communication is bi-directional, but it is possible to make one-way communication as appropriate. As illustrated in FIG. 7, the management server 6 includes an input / output interface 61, a calculation unit 62, a storage unit 63, a display unit 64, and a bus line 65. The calculation unit 62 includes the above-described implementation product distribution calculation unit 9, texture distribution index calculation unit 10, and nonstandard product distribution calculation unit 5. The calculation unit 62 includes a CPU, a ROM, a RAM, an input / output interface, and the like. The storage unit 63 is composed of a hard disk.

  The two-dimensional colorimeter 2 transmits the image A acquired by the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) to the calculation unit 62, and the calculation unit 62 uses the tristimulus value X in the XYZ color system. , Y, Z, and a display device (not shown) that performs an arithmetic process by the conversion process on the image A obtained by the tristimulus values X, Y, Z and displays the visualized image. Since the two-dimensional colorimeter 7 has the same configuration, the description is incorporated.

  The calculation unit 62 calculates the luminance, chromaticity, and the like at arbitrary positions of the acquired images A and B by the two-dimensional colorimeters 2 and 7, respectively, and performs a visualization process. Illumination is applied from obliquely on the surface of the non-standard product NG and the implementation product W, and xy, XYZ, or Lab chromaticity distribution data are compared and indexed.

  With the two-dimensional colorimeters 2 and 7, the surfaces of the non-standard product NG and the implementation product W are usually imaged at one place, and the two-dimensional colorimeter 2 moves as necessary to make another Image at an angle. 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 is a xenon lamp (pseudo sunlight). The lighting unit has a Fresnel lens assembly in addition to the xenon lamp. The xenon lamp is illuminated uniformly from above the surface of the non-standard product NG and the implementation product W. In addition to xenon lamps, LED artificial solar lights may be used.

  The operation of the first client 4 will be described with a specific example. As shown in FIG. 4, it operates by connecting the two-dimensional colorimeter 2 and the management server 6 via the Internet. The connection method can be selected regardless of wired or wireless. A flowchart in the two-dimensional colorimeter 2 is shown in FIG. 8, and a flowchart in the calculation unit 62 is shown in FIG. Since the two-dimensional colorimeter 7 performs the same operation as the two-dimensional colorimeter 2, the description of the operation is cited.

  When the power of the two-dimensional color meter 2 is turned on, initialization is performed as shown in FIG. 8 (initialization S1). Next, the surface of the nonstandard product NG is imaged by the spectral sensitivity (S1 (λ), S2 (λ), S3 (λ)) (imaging processing S2), and then the captured image A is input by the image sensor 23. (S3), and the arithmetic unit 62 converts it into tristimulus values X, Y, and Z (conversion process S4). Spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) are transmitted to the display device (S5). When the image A is a moving image, a series of processes from the imaging process S2 to the data transmission S5 are continuously performed. The image A is displayed on an image display device (not shown). The conversion process S4 may be performed by the non-standard product distribution calculation unit 5 of the management server 6. In this case, the first client 4 transmits the image A to the management server via the Internet I.

  In the imaging process S2, an example of measuring the surface of the non-standard product NG is given, but the non-standard product NG and the practical product W are measured with the two-dimensional colorimeter 2 at different angles for specific areas with different imaging positions. Imaging the surface of 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. In addition, the measurement location is such that the 0-degree optical axis of the two-dimensional colorimeter 2 is perpendicular to the surface of the nonstandard product NG. Moreover, illumination is illumination from diagonally upward like 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 two-dimensional colorimeter 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 process of imaging the surface of the nonstandard product NG with the two-dimensional colorimeter 2 having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) (FIG. 4, FIG. 4). 8). 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 A has values according to the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)), the image A was captured by the conversion process S4 in the calculation unit 62 of the two-dimensional colorimeter 2. The image A 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 two-dimensional colorimeter 2 transmits the values according to the spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) to the calculation unit 62 via the Internet.

  When the management server 6 is powered on, initialization is performed as shown in FIG. 9 (initialization S110). The images A and B transmitted from the first client 4 and / or the second client 8 are received (S120). If the converted tristimulus values X, Y, and Z have been received, the conversion is not performed. If not, the received images A and B are converted into the tristimulus values X, Y, and Z (S140). ). The order of reception is usually the first client 4 first and the second client 8 later. The data is transmitted to a display device (not shown) (display process S150). In accordance with the two-dimensional colorimeter 2 to S120, 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 and the xy texture distribution index of the images A and B of the implementation product W and the nonstandard product NG, and when necessary to display on the display device, The color information is converted into RGB or the like.

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

  A sub-flowchart of S140 in FIG. 10 will be described. The image A of the non-standard product NG of the tile T is captured and stored, and then the image B of the implementation product W of the tile T is captured and stored. Next, the feeling distribution index is sequentially calculated. The similarity of the texture is determined by the texture distribution index from which the texture is separated.

  An inspection region K (see FIG. 20B) corresponding to a region T (see FIG. 8A) to be inspected for the captured images A and B 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 region K cut out from the image A of the imaged nonstandard product NG is created (S143). This chromaticity histogram distribution is an integrated number obtained by counting pixels belonging to the overlapping region D of 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 cell, and an overlapping region D is shown in FIG.

  As shown in FIG. 12 (c), the color distribution to be compared at the position of the xy coordinates is written in a plane, and the inspection area K is partitioned by the 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. Further, if only the specific range of the xy coordinates is calculated 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 tile T is created (S144). In the xy chromaticity histogram distribution, the xy axis is the xy chromaticity, the z axis is the integrated number of pixels, and a planar overlapping region D is shown in FIG.

  For the Lab's a-axis, b-axis, and L-axis, the sum of all the pixels in the inspection region K is taken independently, and the sum of the L value, a value, and b value is divided by the number of pixels, and Lab The average L value, average a value, and average b value of the chromaticity distribution are calculated (S145).

The Lab value converted in Lab space is calculated according to Equation 4 below. The Lab color space is a kind 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. The XYZ value before normalization is converted into the Lab value by Equation 4. In contrast to the distribution in the XYZ color space, the distribution in the Lab color space can be obtained in consideration of the brightness direction.

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 non-standard product NG and the implementation product W is taken as a material for judging the color difference.

As shown in FIG. 10, the center coordinates C ₁ and C ₂ of the xy chromaticity distribution are specified (S146). Here, the center coordinate is the centroid (center of gravity position).

As shown in FIG. 10, the deviation ΔF of the center coordinates is set so that one of the two xy histogram distributions H ₁ (x, y) and H ₂ (x, y) matches the other center coordinates. Therefore, the entire xy chromaticity distribution is shifted (mapped) (S147). If one of the distributions is not shifted to the other distribution, the difference between the color components is also calculated. You can do it 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 texture distribution index indicating a spatial spread difference and a color index such as a color difference ΔE are also 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 texture distribution index is calculated by the following formula. The xy chromaticity histogram distribution is the cumulative number of pixels. FIG. 12D shows the overlap region D, and FIG. 12E shows the minimum distribution.
Texture distribution index = total number of pixels belonging to overlapping region D / total number of pixels in inspection region K × 100 (%)

  Since the spread degree histogram of the images A and B in the two-dimensional space and the minimum value of the same position in the array is taken is the overlap frequency, this value is divided by the total histogram total count. Calculated with

FIGS. 12D and 12E are cross-sectional views of FIG. 12C taken along the line SS, 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. Figure 12 (d) of cumulative number _{H 1} and the accumulated number of _{H 2} Each image A, corresponding to the image B. When the two histogram distributions are compared, an overlap region D exists.

As shown in FIG. 12E, H ₁ (x ₁ , y ₁ ) is the cumulative number of xy chromaticity histogram distributions of the nonstandard product NG, and H ₂ (x ₂ , y ₂ ) is the xy color of the nonstandard product NG. When the accumulated number of degrees histogram 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 area K is determined, and the non-standard product NG and the tile T both have the same total number of pixels. The calculation of this ratio may be integrated three-dimensionally for all the lattices G. For example, as shown in FIG. 12C, 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. 12F 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 images A and B are cut out in the same inspection area K, the total number of pixels of the images A and B is 10,000 pixels. From the xy chromaticity histogram, the number of pixels in the overlapping region D is integrated, and when the integrated number is 5,000, the texture distribution index is 50%. As the texture distribution index falls below 100%, the degree of difference in texture 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 matching about a texture.

  For the images A and B, 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. Compared with the case of acquiring by means of high accuracy and faithful 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 textures of the images A and B are separated from the colors and can be grasped by a histogram distribution, it is possible to determine even a subtle difference in hue by reflecting differences in surface gloss, gloss, unevenness, roughness, and the like.

  For example, as shown in FIGS. 13A to 13C, an example will be described in which three types of non-standard products NG are compared from a small roughness to a large roughness. A low roughness is “1”, a medium roughness is “2”, and a high roughness is “3”. First, when the distribution on the xyz chromaticity diagram after the non-standard products NG1 to NG3 were subjected to the above-described processing was created, as shown in the xy chromaticity diagram of FIG. It is data. The accumulated number is shown in light and dark. The brighter the color, the larger the accumulated number. FIG. 13C schematically shows the integrated number in three dimensions of the implementation product W and the nonstandard product NG. The xy axis is chromaticity, and the z axis is the cumulative number. Basically, the larger the roughness, the lower the mountain shape, and the finer the roughness, the sharper mountain shape. For “1”, “2”, or “3”, the texture distribution index indicating the degree of overlap is calculated by comparing the histogram distributions of the implementation product W. Specific examples will be shown in Examples described later.

  Next, the color / texture management system for the surface of the non-standard product NG and the implementation product W according to the second embodiment will be described with reference to FIGS. FIG. 14 uses XYZ color space coordinates, and FIG. 15 uses Lab color space coordinates. The corresponding steps are referred to as the 200s and 300s, and the differences are mainly described.

FIG. 14 is a flowchart for calculating a texture distribution index based on a comparison of chromaticity histogram distributions from two images A and B. When the program is activated, the inspection area K is cut out from the image A, specified, and set (S201). An inspection area K similar to 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 chromaticity histogram distributions of the non-standard product NG and the implementation product W are calculated and created (S204). An average value of the XYZ values is calculated (S205). The center coordinates of the XYZ color space distribution are specified (S206). The XYZ color space distribution to the center coordinates is shifted (S207). After the shift process, the center coordinates of the XYZ color space distribution are specified (S208). This is to confirm the suitability of the center coordinates after the shift process. Here, the center coordinates can be readjusted. The minimum distribution of the XYZ chromaticity histogram distribution is specified, and the integrated number of the XYZ chromaticity histogram distribution in the overlapping region D is calculated (S209). Texture distribution index = (integrated number of pixels belonging to overlapping region D / total number of pixels in inspection region K) × 100 (%). Cumulative number of in the overlapping area D of T ₁ and T _2, adds calculates the cumulative number of lesser. The texture distribution index is calculated, the color index such as the color difference ΔE is also calculated (S210), and the process returns.

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. 16A and 16B, the XYZ values in the XYZ space coordinates of the non-standard product NG and the implementation product W are T ₁ (L, a, b) and T ₂ (L, a, b). In the XYZ color space, the histogram distribution has a globe-like shape, and the two histogram distributions may be three-dimensionally overlapped or separated. This is shifted to bring the center coordinates closer. The inspection area K in the three-dimensional space is partitioned by a grid, and the chromaticity 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 may be projected on a plane, and the accumulated number of overlapping regions on the grid may be calculated by the same accumulation within the plane. In the case of XYZ chromaticity, since there is no brightness information, the histogram distribution does not change even if the brightness of the images A and B changes in the XYZ space.

  When the Lab color space histogram is used for texture determination instead of the XYZ color space histogram, the flowchart of FIG. 15 is used. The description of FIG. 15 uses the above description of FIG. In S205, the average Lab value of the area and the average Lab value of the inspection area K of the image B are calculated. In the case of Lab chromaticity, since there is brightness information, the histogram distribution changes when the brightness of the images A and B changes in the Lab space.

  Next, a color / texture management system according to the third embodiment will be described with reference to FIG. For corresponding similar elements, the description is incorporated, and the differences are mainly described.

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 chromaticity distribution is a solid elliptical shape. As shown in FIGS. 17A and 17B, Lab values of the non-standard product NG and the implementation product W are expressed as U ₁ (L, a, b), U ₂ (L, a, b). 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, when the brightness of the images A and B changes in the Lab space, the L value changes, and the distributions U ₁ and U _{2 of the} matching degrees are in the Lab space. Since the position is shifted, the determination in consideration of light and dark is possible. This is because if the brightness of the images A and B is different, the position of the distribution is shifted. For example, the Lab chromaticity histogram distribution shifts downward when dark and shifts upward when bright.

  Next, the color / texture management method of the first embodiment will be described with reference to FIG. Although it is a color / texture management method for housing construction, it can also be applied to other buildings such as condominiums.

  (1) In the inspection process of the manufacturing factory M, inspection is performed and non-standard product NG is selected.

  (2) The non-standard product NG is imaged by the two-dimensional colorimeter 2, and the image A having three spectral sensitivities acquired from the non-standard product NG is stored in the storage unit 3 of the first client 4.

  (3) The product specific information ID and the image A of the non-standard product NG are stored in the tag 15, and the non-standard product NG with the tag 15 attached to the distribution warehouse company that owns the third client 12 is stored.

  (4) The product unique information ID and the image A are transmitted to the tag 15 from the first client 4 to the management server 6 over the Internet I.

  (5) The house J constructed by the construction company X fades with the passage of time. And since the implementation product W which is a part of the tile T was damaged and it was necessary to replace the implementation product W, the landlord contacted the construction shop X, and the construction shop X was damaged by the two-dimensional colorimeter 7. The implementation product W is imaged and transmitted from the second client 8 to the management server 6 over the Internet I.

  Instead of imaging the execution product W itself, in order to match the color and texture of the peripheral portion, the periphery other than the damaged portion may be imaged and the color distribution data may be calculated.

  (6) Since the management server 6 searches for the non-standard product NG that approximates the implementation product W, the histogram of the non-standard product NG having the same product unique information ID and the implementation product W is stored in the database DB. Compare the distributions, calculate the texture distribution index, identify a candidate with a high value, and select an appropriate nonstandard product NG that approximates the color / texture together with color information such as the color difference ΔE. The second client 8 receives this selection result on the Internet I, and the construction shop X confirms it. The images A and B may be confirmed while viewing the actual product at the house J as necessary. After confirming the construction shop X, the construction shop X transmits the product specific information ID corresponding to the nonstandard product NG corresponding to the distribution warehouse company over the Internet (may be transmitted from the management server 6), and the third client 12 The output unit 11 uses the tag 15 to search for the corresponding nonstandard product NG in the warehouse automatically, for example, ships from the data P of thousands to tens of thousands of nonstandard product NG. To do.

  In addition to the product unique information ID, if the combined information of the address of the construction company X of the destination is transmitted, the distribution warehouse company can refer to the ID, address and name, so that it becomes more efficient.

  (7) The construction shop X goes to the house J and replaces it with a nonstandard product NG that approximates the product W. The above series of processing ends.

  As described above, a color space histogram distribution of non-standard product NG is created, stored in the database DB and the tag 15 together with product specific information, and the tags 15 are pasted to the non-standard product NG one by one. Store in a distribution warehouse company. By using the tag 15, a corresponding non-standard product NG having appropriate color and texture can be shipped as a replacement.

  The present embodiment is a mechanism for reusing non-standard products NG, and storing and utilizing non-standard products NG that have been discarded and discarded in the past.

  Compared with the case where this is made by order, the cost can be significantly reduced. When the product W is replaced due to aging, it is practically impossible to manufacture a product that has a color that fits perfectly even though the stain can be removed. Thereby, various claims can be avoided. In particular, since tiles are baked goods, they are not known unless they are actually baked, and there are not a few variations, so baked goods that do not pass inspection are discarded. Such waste can be avoided.

  For example, the present invention can be applied to parts such as bumpers in other industries such as the automobile industry. For example, a bumper maker applies paint and measures color. Bumper non-standard NG can be stored without being discarded, and a bumper with similar color and texture can be selected as much as possible.

  For example, by using this system, it is possible to register a bumper of a car that has been scrapped as a non-standard product NG and use it as a replacement for another bumper, thereby adding value to the product.

  Next, Embodiment 4 of the present invention will be described with reference to FIGS. The fourth embodiment is an example applied to a bumper, an instrument panel, a seat, and a door panel that are automobile parts instead of the tile T, and others are common to the first to third embodiments.

  Next, a color / texture management system according to the fifth embodiment will be described with reference to FIG.

  As shown in FIG. 20, the RGB color display of the RGB display unit 502 that is an RGB color display incorporated in the wearable terminal 503 displays candidate RGB color / texture data, and linearity of signals in each RGB channel. Characteristics, color development / texture characteristics are measured, and correction terms such as crosstalk of color / texture data in this mixed color are calculated. At the same time, XYZ color space measurement is performed by a micro-optical system reading spectroscope (small high-precision spectroscope) 509 to acquire highly accurate XYZ correction data. The management server 506 corrects the XYZ data from the small high-precision spectrometer 509 by the color / texture correction unit 511 and stores the correction data in the storage unit 512. A signal is output from the management server 506 to the signal pattern generator 513, and this signal is transmitted to the RGB display unit 502.

  The color / texture management system according to the sixth embodiment corrects the spectral sensitivity based on the color temperature because the color temperature which is one of the photographing environments of the two-dimensional colorimeters 2 and 7 according to the first to fourth embodiments is different. is there. A sixth embodiment will be described with reference to FIGS. Color image measuring unit R that measures the color temperature with the corresponding color temperature measuring sensors E and F when the non-standard product NG and the implementation product W are imaged with the two-dimensional colorimeters 2 and 7 in the respective environments. And an illumination color conversion correction unit Q that converts the images A and B into corrected images subjected to color temperature correction based on the measurement values of the color temperature measuring sensors E and F. Also, instead of tiles, it is applied to the case of car bumper color / texture management.

  The color temperature measuring sensors E and F include a micro-spectrometer, and calculate and output a color temperature from the measurement data of the spectroscope. An example of the micro-spectrometer is C12666MA manufactured by Hamamatsu Photonics. This is a fingertip-sized micro-spectrometer head that combines MEMS technology and image sensor technology, and has a sensitivity wavelength range of 340 to 780 nm and a wavelength resolution of 15 nm max. The object is imaged by the micro color temperature measuring sensors E and F, and the spectral sensitivity characteristic, that is, the relative sensitivity (%) that is the output value of the spectrum with respect to the wavelength in the sensitivity wavelength range is obtained.

  The calculation unit 62 of the management server 6 has a color temperature measurement unit R, the two-dimensional color meter 2 images a non-standard product NG, measures the color temperature with the temperature sensor-E, while the two-dimensional color meter 7 The actual product W is imaged, the color temperature is measured by the temperature sensor-F, and the three-band visual sensitivity images S1i, S2i, S3i (T = 6500K) (i = 1 to m, m) are stored in the storage unit 63 of the management server 6. Is the number of pixels). The environment of the implementation product W side is T = 6500K, the environment of the non-standard product NG side is T = 2900K, and the illumination color conversion correction unit Q determines the 3-band visual sensitivity image S1i, S2i and S3i are corrected.

  The flowcharts in the management server 6 are shown in FIGS. 9 and 10, respectively.

  As shown in FIG. 9, initialization is performed (S510). A plurality of three-band visual sensitivity images S1i, S2i, and S3i (T = 6500K, T = 2900K) relating to the nonstandard product NG and / or the implementation product W respectively transmitted from the two-dimensional colorimeters 2 and 7 are received, and the storage unit 63 is recorded as 3-band visual sensitivity images S1i, S2i, S3i (S520). When the image is a moving image, a series of processing is continuously performed.

  The three-band visual sensitivity images S1i, S2i, S3i in which color / texture information is registered corresponding to the product unique information ID (product type, etc.) of the IC tag 515 are read and transmitted to the illumination color conversion correction unit Q (S530). ).

  It is determined whether a registration request has been received (S540). If YES, the process proceeds to S550, and if NO, the process proceeds to a display process (S560).

  When receiving an image registration request from the two-dimensional colorimeters 2 and 7, the three-dimensional visual sensitivity images S1i, S2i, and S3i imaged and recorded by the two-dimensional colorimeters 2 and 7 are newly registered or updated (S550). .

  Processing related to registration is displayed on the display unit of the management server 6 (S560).

  It is determined whether to end the process. If the process does not end, the process returns to S520. If the process ends, the process returns (S570).

  The processing of the illumination color conversion correction unit Q will be described with reference to FIG. Color temperature correction processing using ambient light is applied to the color and texture management of car bumpers.

  The received three-band visual sensitivity images S1i, S2i, S3i (T = 6500K) are subjected to temperature correction for each pixel in the calculation unit 62, converted into tristimulus values XYZ, and RGB images are converted by XYZ-RGB conversion. Transmit to one client 4, second client 8, and / or third client 12. The XYZ conversion is performed according to Equation 2 on the implementation product W side, but on the non-standard product NG side where the environment is different from that of the implementation product W side, the conversion is performed in consideration of the correction according to Equation 3 because of temperature correction. The tristimulus values XYZ of the XYZ color faithful image are corrected to tristimulus values X ′, Y ′, and Z ′ as shown in Equation 3, and based on these correction values, the image on the implementation product W side, the nonstandard product NG The side image is displayed.

Here, the matrix coefficient correction at T = 6500K is performed so that the heads of the curves of the spectral sensitivities S1, S2, and S3 appearing in the conversion formula of XYZ-S1, S2, and S3 of the following formula 2 are aligned. . For details, see IEICE TRANS.INF. & SYST., VOL.E93-D, No.3 MAR 2010 copy rght 2010 The Institute of Electronics, Information and Communication Engineers, Development of an XYZ Digital Camera with Embedded Color Calibration System for Accurate Refer to Color Acquisition, Maciej KRETKOWSKI, Ryszard JABLONSKI, SHIMODAIRA P651-653 for white balance and color calibration.

  The illumination color conversion correction unit Q performs ambient light correction on the implementation product W side and the nonstandard product NG side. For example, T = 6500K in the environment on the implementation product W side, and in the environment on the non-standard product NG side, for example, the environmental color temperature is assumed to be T = 5000K for explanation, but is actually measured by the micro color temperature measurement sensors E and F. Therefore, the color temperature is not particularly relevant, but the purpose is to perform correction based on the temperature difference to improve the accuracy of the color / texture data. The illumination color conversion correction unit Q on the non-standard product NG side, and the configuration of the color temperature measuring sensors E and F, which can be connected to the illumination color conversion correction unit Q and provided with a small spectroscope, for example, a microspectrometer, are used. Hereinafter, a description will be given with reference to FIGS.

  When using the color temperature of the actual lighting measured by the color temperature measuring sensors E and F, their spectra do not necessarily match (Color Engineering, Noboru Ota (Tokyo Denki University Press)) Even if it is not derived from the spectral sensitivity of S1, S2, S3 from the black body radiation from the color temperature, the three numerical values of the illumination light on the implementation product W side and non-standard product NG side are: S1gain and S3gain can be calculated using the three values obtained by multiplying the spectral sensitivity of S1, S2, and S3 by the illumination spectrum measured on the product W side and non-standard product NG side. The actual spectrum and the actual spectrum measurement can be arbitrarily selected for the implementation product W side and the nonstandard product NG side, respectively, and the spectral characteristics output of the implementation product W side illumination S123 and the nonstandard product NG side illumination S123. It is the characteristic to obtain | require ratio of.

  As shown in FIGS. 10 to 12, the illumination color conversion correction unit Q is configured for the three-band visual sensitivity images S1i, S2i, S3i (T = 6500K) of the nonstandard product NG and the implementation product W recorded by the management server 6. The temperature is corrected from T = 6500K to T = 5000K, and the calculated image is transmitted to the two-dimensional colorimeter 3.

  Processing performed by the illumination color conversion correction unit Q will be described. In S600, the non-standard product NG side displays the 3-band visual sensitivity images S1i, S2i, and S3i (T = 6500K) obtained by imaging the vehicle on the implementation product W side with the two-dimensional colorimeter 207 from the implementation product W side. Receive. On the implementation product W side, as shown in FIG. 11, when the output value of the illumination spectrum of 6500K is multiplied by the spectral sensitivities S1, S2, and S3 (see Equation 1 and FIG. 5), the multiplied values S1 and S2 as shown by diagonal lines. , S3 is obtained. These are integrated to calculate the integral values IS1, IS2, IS3 within the curve range indicated by the oblique lines, and these integral values are adjusted, for example, two-dimensional colorimeters 2, 7 so that IS1 = IS2 = IS3 The gain is adjusted to be the normalized sensitivity of the two-dimensional colorimeters 2 and 7.

  In S610, S1gain, S2gain (which is relatively 1) and S3gain at T = 5000K are calculated by the color temperature measurement sensors E and F on the non-standard product NG side. As shown in FIG. 12, the output value of the illumination spectrum of T = 5000K on the non-standard product NG side (5000K is an example and other temperatures may be used) is normalized sensitivity S1, S2, S3 (see Equation 1, FIG. 5). ), And integrating these values to calculate integral values I03, I02, I01 within the curve range indicated by diagonal lines, and obtain S1gain = I01 / I02 and S3gain = I03 / I02. This calculation simplifies the calculation of the gain value obtained by the next calculation on the nonstandard product NG side. Even if you do not adjust the gain, set S1gain = (IO1 / IO2) / (IS1 / IS2), S3gain = (IO3 / IO2) / (IS3 / IS2) If the gain calculation on the non-standard product NG side transmits the integral value on the implementation product W side to the non-standard product NG side, the calculation on the non-standard product NG side becomes possible based on that. As described above, since it is not necessary to perform more calculations for preparation in advance to align the heads of S1, S2, and S3 like the matrix coefficient correction described in Equation 2, such gain adjustment is performed. By doing so, the S1, S2, and S3-XYZ conversion formulas of the above-described formula 2 are associated with the illumination on the implementation product W side.

  In S620, the three-band visual sensitivity images S1i, S2i, S3i (T = 6500K) acquired in S600 are corrected by S1gain and S3gain calculated in S610, and S1i and S3i are corrected, and the three-band visual sensitivity images S1i5000k, S2i5000k (same as S2i) ), S3i5000k (T = 5000K). This is the same calculation as in Equation 3.

In S630, the light incident portions (slits) of the color temperature measuring sensors E and F are pressed against the image displayed on the screen of the display portion to capture the image, and R data (for example, R = 255: 8 bits) Create a conversion table (can be a conversion matrix) to convert from XYZ values to RGB values by linking with values, linking G data with Xg, Yg, Zg, B data with Xb, Yb, Zb. To do. This process takes ambient light on the nonstandard NG side into the illumination color conversion correction unit Q via the color temperature measurement sensors E and F, and the ambient light spectrum on the nonstandard NG side on the color temperature measurement sensors E and F. Is accurately reflected in the XYZ-RGB conversion, and the RGB data is displayed by the color temperature measuring sensors E and F, and the spectrum for each is acquired. As a result, X _R , Y _R , and Z _{R for R} are obtained. Similarly, when G and B are obtained, X _G , Y _G , Z _G for _G and X _B , Y _B , Z for _{B B} asks. In this way, a conversion table from XYZ to RGB is obtained. About S step 630, you may process and program beforehand.

  In S640, the accurate relative 5000k of each image is obtained from S1i5000k, S2i5000k, and S3i5000k (i = 1 to m, m is the number of pixels) obtained by multiplying the spectral relative gains S1gain, S2gain, and S3gain (T = 5000K) obtained in S620. The XYZ value (T = 5000K) is obtained. The calculation is performed according to Equation 2 described above.

  In S650, the XYZ value (T = 5000K) obtained in S640 is used to obtain the corresponding RGB value (T = 5000K) using the XYZ-RGB conversion table. By the above color temperature correction calculation, an image can be accurately displayed on the RGB monitor in accordance with the environmental change.

  In S660, the RGB value (T = 5000K) obtained in S650 is transmitted to the first client 4, the second client 8, or the third client 12, and an image corresponding to the RGB value is displayed on the display unit.

  According to the sixth embodiment described above, the color / texture environment correction is performed by the illumination color conversion correction unit Q, so that the color / texture has a high advantage in quality, and the color / color on the implementation product W side and the non-standard product NG side. You can share the texture. There is an advantage that a worker with a low level of skill in color / texture judgment can immediately perform advanced inspections on color / texture judgment.

  Costs can be reduced by improving the efficiency of the color / texture matching process, and cost can be reduced by reducing waste of color / texture correction. In addition, the introduction cost is low, and it is stable with high accuracy.

  Although the example applied to the replacement assembly process of the bumper to the car at the finished car factory was given, it is of course possible to apply it to the bumper replacement at the parts factory.

  Quickly unify the color and texture of the combined body. With the IOT technology, the color / texture of the car being carried can be reused because the information is described in the IC tag 515 attached to the part.

  Next, a color / texture management system according to Embodiment 7 of the present invention will be described with reference to FIG. The seventh embodiment is generally common to the first embodiment, but uses the two-dimensional colorimeter 2 in the inspection process of the manufacturing factory M and directly transmits measurement data to the management server 6. Since the two steps can be combined into one, the work can be simplified. In addition, since the color space histogram distribution is calculated by the two-dimensional colorimeters 2 and 7 and transmitted to the management server 6, the calculation time in the management server 6 is shortened. In addition, since another structure is common in Embodiment 1, and Embodiments 2-6 can also be utilized suitably, the description uses the above.

  The seventh embodiment is performed in the following procedure. Since the configuration change is obvious to those skilled in the art, the description thereof is omitted.

  Next, the color / texture management method of the seventh embodiment will be described with reference to FIG. Although it is a color / texture management method for housing construction, it can also be applied to other buildings such as condominiums.

  (1) In the inspection process at the manufacturing factory M, the two-dimensional color meter 2 is used to inspect non-standard products NG.

  (2) The image A having three spectral sensitivities acquired from the nonstandard product NG in (1) and the color space histogram distribution from the image A are stored in the storage unit 3 of the first client 4.

  (3) At the manufacturing factory M, the product specific information ID and the image A of the nonstandard product NG are stored in the tag 15, and the nonstandard product NG with the tag 15 attached to the distribution warehouse company that owns the third client 12 is transported. And keep it.

  (4) The product unique information ID, the image A, and the color space histogram distribution are transmitted to the tag 15 from the first client 4 to the management server 6 over the Internet I.

  (5) The house J constructed by the construction company X fades with the passage of time. And since the implementation product W which is a part of the tile T was damaged and it was necessary to replace the implementation product W, the landlord contacted the construction shop X, and the construction shop X was damaged by the two-dimensional colorimeter 7. The execution product W is imaged, the image B is acquired, the color space histogram distribution is calculated, and the image B and the color space histogram distribution are transmitted from the second client 8 to the management server 6 via the Internet I.

  Instead of imaging the execution product W itself, in order to match the color and texture of the peripheral portion, the periphery other than the damaged portion may be imaged and the image and color distribution data may be calculated.

  (6) Since the management server 6 searches for the nonstandard product NG that approximates the implementation product W, the color of the nonstandard product NG having the same product unique information ID and the implementation product W among those already stored in the database DB The spatial histogram distributions are compared, the texture distribution index is calculated, the one with the highest value is identified, and the nonstandard product NG that approximates the color / texture is selected together with the color information such as the color difference ΔE. The second client 8 receives this selection result on the Internet I, and the construction shop X confirms it. The images A and B may be confirmed while viewing the actual product at the house J as necessary. After confirming the construction shop X, the construction shop X transmits the product specific information ID corresponding to the nonstandard product NG corresponding to the distribution warehouse company over the Internet (may be transmitted from the management server 6), and the third client 12 The output unit 11 uses the tag 15 to search for the corresponding nonstandard product NG in the warehouse automatically, for example, ships from the data P of thousands to tens of thousands of nonstandard product NG. To do.

  In addition to the product unique information ID, if the combined information of the address of the construction company X of the destination is transmitted, the distribution warehouse company can refer to the ID, address and name, so that it becomes more efficient.

  (7) The construction shop X goes to the house J and replaces the product W with a non-standard product NG approximating it. The above series of processing ends.

  As described above, the same effects as those of the first embodiment can be obtained, the image A and the color space histogram distribution are calculated in the inspection process, and the tags 15 are attached to the nonstandard products NG one by one, and the distribution warehouse Since it is stored in the company, the work is simplified.

  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, the two-dimensional colorimeter is not limited to these, and the technical idea of the present invention can be implemented by other types of two-dimensional colorimeters. For example, for the method of acquiring image information according to the three-band visual sensitivity images S1i, S2i, and S3i, the method given in the present embodiment is only a specific example.

  Other examples can be implemented. For example, the two images A and B acquired for the implementation product W and the nonstandard product NG are overlapped, and each chromaticity histogram distribution is displayed on the display device, or each chromaticity histogram distribution is displayed as one Overlaid on the chromaticity diagram, the chromaticity diagram can be displayed, and the color difference can be determined by the average Lab value, while the texture distribution index calculation indicating the surface texture can be separated and displayed as a percentage. As a result, it is possible to reliably confirm the deviation of the spatial spread of the chromaticity distribution of the implementation product W of the non-standard product NG, in particular, the unevenness and the roughness, 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 reduce the problems of individual differences that cannot be avoided by visual inspection, troubles of judgment standards with customers, etc., and to standardize the texture finish and to perform stable texture management.

  For example, because non-contact and wide-area photography is possible, non-standard product NG and implementation product W can be imaged from flat angles, and flip-flops and surface roughness can be digitized. Evaluation that is close to the color and texture of the eyes is possible. Parts with irregular patterns such as wood grain panels can also be matched in color and texture. Since the captured images A and B can be displayed on the display (overlay function), alignment can be performed easily. The non-standard product can be compared with the implementation product W even if the size and material are different. Fabrics with irregular patterns and textures such as leather can also be matched in color and texture. 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 also check the texture of teeth in the dental field.

  Hereinafter, the present invention will be specifically described with reference to examples, but the present invention is not limited thereto. In addition, the measurement and evaluation of the characteristic value in an Example were performed as follows.

(1) Evaluation device A two-dimensional color meter PPLB-500 of Paparabo Co., Ltd. was used. Lighting was Panasonic D50 lighting.

(2) Shooting
The PPLB-500 was shot in a dark room. The two-dimensional colorimeter was a still image type, and the measurement was performed with the L value of the white plate set to 100.

(3) Measurement range Regarding the measurement range of the sample, the range used for evaluation was the same size for all samples. The measurement range is within the frame of the image.

(4) Measurement items and results Sample 1 (reference), sample 2, and sample 3 obtained from an image of the building outer wall are used. Sample 1 is an image B relating to the implementation product W taken at the house J. Samples 2 and 3 correspond to the image A related to the nonstandard product NG. In the sample 1, the upper left tile corresponds to the implementation product W, and the other tiles are tiles arranged around the tile. Samples 2 and 3 are composite images by a graphic technique in which the upper left tile is replaced with a nonstandard NG tile. Sample 1 has reference product W at the upper left, and samples 2 and 3 have nonstandard product NG at the upper left.

  Differences in coincidence, ΔE, and average Lab of samples 1 to 3 were obtained. Among the texture distribution indexes, Lab is the degree of coincidence at the Lab coordinate (the center coordinate is not shifted (S147 in FIG. 10, S207 in FIG. 14, S207 in FIG. 15)), and xy3D is the degree of coincidence in the xy coordinate ( Texture is the degree of coincidence calculated by shifting the center coordinates by the average value of the respective xy3D histogram distributions. Difference of average Lab value = average Lab value of sample 2 or 3−average Lab value of sample 1 From the measurement result, the sample 2 or 3 having the most similar color and surface texture to the sample 1 was specified from the result of the degree of coincidence and the numerical value of ΔE. For ΔE, ΔE00 was used in consideration of human visibility characteristics.

  The entire image of samples 1 to 3 is a comparative inspection, and an evaluation for selecting a non-standard tile NG for replacing the tile is obtained.

  Sample 2 and sample 3 were inspected with reference to sample 1 for the entire inspection images of samples 1 to 3.

  By comparing images that include a plurality of tiles around the implementation product W instead of comparing one tile at a time, it is possible to compare the discomfort of the entire combination.

  Depending on how the tiles are combined, it can be judged that it is generally appropriate or not.

The entire images A and B are designated as the inspection region K. The inspection results are shown in Table 1 and FIG.

  When samples 1 to 3 are subjected to inspection, as shown in FIG. 26, the Lab matching degree of samples 1 and 2 is 31% and the texture is 89%, whereas the xy3D matching degree of samples 1 and 3 is 21%. The texture has dropped to 80%. The ΔE of Samples 1 and 2 was 0.112, whereas the ΔE of Samples 1 and 3 was 0.472, and the enlargement increased. Therefore, it can be seen from the results of the Lab coincidence, Texture, and ΔE that the sample 2 is closer in color and texture to the sample 1 than the sample 3. When exchanging the tile of sample 1, the tile at the upper left of sample 2 is selected.

  This is an inspection comparing Samples 1 to 3 with the upper right, lower left, and lower right tiles based on the upper left tile. This is because it is used as a reference for selecting an approximate tile.

Sample 1 was compared with the upper right, lower left, and lower right tiles based on the upper left tile. There are three combinations: upper left-upper right, upper left-lower left, upper left-lower right. A rectangular frame of the image B is designated as the inspection area K. The inspection results are shown in Table 2 and FIG. The following can be seen from the test results.

  When the samples 1 to 3 are inspected, as shown in Table 2 and FIG. 27, the Lab matching degree is 84% in the upper left and upper right of the sample 1, 0% in the upper left and lower left, and 0% in the upper left and lower right. The upper left and upper right of sample 2 are 50%, the upper left and lower left are 29%, the upper left and lower right are 28%, the upper left and upper right of sample 3 are 0%, the upper left and lower left are 88%, and the upper left and lower right are 85. %. ΔE is 0.200 in the upper left and upper right of sample 1, 4.296 in the upper left and lower left, and 4.164 in the upper left and lower right. Therefore, it can be seen from the results of the Lab matching degree and ΔE that the sample 2 is closer in color and texture to the sample 1 than the sample 3 in relation to the surrounding tiles. When exchanging the tile of sample 1, the tile at the upper left of sample 2 is selected.

  Since the inspection results described above were correlated with visual evaluation, it is possible to inspect the color / texture of multiple tiles as a whole, or between tiles and other surrounding tiles, allowing a wide variety of color / texture inspections. It turns out that.

  It is possible to support industries that are in trouble with waste disposal such as tiles, such as condominium developers and various manufacturing industries.

A, B Image B Bumper C ₁ Center coordinate D Area DB Database E, F Color temperature measuring sensor G Grid H Histogram distribution H ₁ Accumulated number H ₂ Accumulated number I Internet ID Product specific information J Housing K Inspection area Lab Average M Manufacturing factory NG Non-standard product Q Illumination color conversion correction unit R Color temperature measurement unit T Tile U ₁ Distribution W Product ΔE Color difference ΔF Deviation 1 Color / texture management system 2 Two-dimensional colorimeter 3 Storage unit 4 First client 5 Non-standard product distribution 6 Management Server 7 2D Colorimeter 8 Second Client 9 Practical Product Distribution Calculation Unit 10 Texture Distribution Index Calculation Unit 11 Output Unit 12 Third Client 15 Tag 21 Shooting Lens 22a Optical Filter 22b Optical Filter 22c Dichroic Mirror 23 Image Sensor 23a- 23c Image sensor 26 Reflector 27 Filter turret 61 Input / output interface 62 arithmetic unit 63 storage unit 64 display unit 65 bus line

As shown in FIG. 4, the two-dimensional colorimeter 2 has a photographing lens 21, three optical filters 22a, 22b, and 22c arranged behind the photographing lens 21, and a rear of the optical filters 22a, 22b, and 22c. And an image pickup device 23 (CCD, CMOS, etc.) arranged. The three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) of the two-dimensional colorimeter 2 are obtained by multiplying the spectral transmittances of the optical filters 22a, 22b, 22c and the spectral sensitivity of the image sensor 23. Is given. The arrangement relationship between the optical filters 22a, 22b, and 22c and the image sensor 23 in FIG. 4 is merely schematically shown. Specific examples of methods for acquiring images according to the three spectral sensitivities (S1 (λ), S2 (λ), and S3 (λ)) will be given below. In the first embodiment, any of these can be adopted. Also, other methods can be adopted. Reference numeral 24 denotes a calculation unit, and 25 denotes a display unit.

FIG. 6A 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 (13)

The first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from non-standard products. A storage unit for acquiring and storing the three-band visual sensitivity images S1i, S2i, S3i;
The coordinates corresponding to the color space of the XYZ color system are stored from the non-standard three-band visual sensitivity images S1i, S2i, S3i, the coordinates are partitioned by grids, and the number of pixels of the inspection surface belonging to each grid is determined. A non-standard product distribution calculation unit that creates a color space histogram distribution of the XYZ color system by integrating, and stores the color space histogram distribution and the product-specific information corresponding to the database in association with each other;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, and S3i are acquired, coordinates corresponding to the color space of the XYZ color system are stored from the three-band visual sensitivity images S1i, S2i, and S3i of the above-described product, and the coordinates are partitioned by a grid. Then, by integrating the number of pixels of the inspection surface belonging to each grid, to create a color space histogram distribution of the XYZ color system and store the color space histogram distribution,
A texture distribution index calculation unit that compares and determines the distribution of the non-standard product and the distribution of the implementation product and calculates a texture distribution index;
An output unit that refers to the texture distribution index and identifies and outputs the product-specific information of a non-standard product that approximates the color / texture to the implementation product,
A color / texture management system, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet. The first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from non-standard products. Three-band visual sensitivity images S1i, S2i, S3i are acquired, and the XYZ values of the pixels of the three-band visual sensitivity images S1i, S2i, S3i from the non-standard three-band visual sensitivity images S1i, S2i, S3i, or A storage unit for storing xy values normalized from the XYZ values;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution calculation unit that stores a degree histogram three-dimensional distribution or a Lab chromaticity histogram three-dimensional distribution in the database of the management server in association with the product-specific information corresponding to the color space histogram distribution;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, S3i are acquired, and the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram are partitioned by a grid, and the number of pixels of the nonstandard product belonging to each grid is integrated. An actual product distribution calculation unit that calculates the xy chromaticity histogram distribution, the XYZ chromaticity histogram three-dimensional distribution, or the Lab chromaticity histogram three-dimensional distribution created by
A texture distribution index calculation unit that compares and determines the distribution of the non-standard product and the distribution of the implementation product and calculates a texture distribution index;
An output unit that refers to the texture distribution index and identifies and outputs the product-specific information of a non-standard product that approximates the color / texture to the implementation product,
A color / texture management system, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet. A management server that has a database and manages color and texture;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from non-standard products. A first client that stores the three-band visual sensitivity images S1i, S2i, S3i and transmits them to the management server via the Internet;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from the product 3 A second client for storing the band visual sensitivity images S1i, S2i, S3i and transmitting the images to the management server via the Internet;
A management server for receiving three-band visual sensitivity images S1i, S2i, S3i from the first and second clients via the Internet and storing them in a database;
With
The non-standard product and the three-band visual sensitivity images S1i, S2i, and S3i of the product are normalized by the XYZ values of the respective pixels of the three-band visual sensitivity images S1i, S2i, and S3i, or the XYZ values, respectively. Store the xy value,
An xy chromaticity histogram distribution created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the non-standard product and the implemented product belonging to each grid. , XYZ chromaticity histogram three-dimensional distribution, or Lab chromaticity histogram three-dimensional distribution is stored in the database of the management server,
For the distribution of the non-standard product, the distribution and the product-specific information corresponding to the distribution are stored in association with each other,
Compare and determine the distribution of the non-standard product and the distribution related to the implementation product, calculate a texture distribution index,
With reference to the index, the product-specific information of a non-standard product whose color / texture approximates the implementation product is output from the first client.
A color and texture management system characterized by this.   Instead of calculating the distribution at the management server, the first client and the second client may use the three-band visual sensitivity images S1i, S2i, S3i and the distribution calculated from the images as Internet The color / texture management system according to claim 3, wherein the data is transmitted to the management server via a network.   The color / texture management system according to any one of claims 1 to 4, wherein a tag is attached to the non-standard product, and the distribution of the non-standard product and the product-specific information are stored in the tag.   The distribution belonging to the non-standard product stored in the tag and read out is compared with the distribution related to the implementation product, and the product-specific information of the non-standard product whose color / texture approximates the implementation product is determined. 6. The color / texture management system according to claim 5, which is transmitted to two clients. The first client
A color temperature measuring unit for measuring the color temperature by the first color temperature measuring sensor;
The second client
A color temperature measuring unit for measuring the color temperature by the second color temperature measuring sensor;
Illumination that converts the three-band visual sensitivity images S1i, S2i, and S3i of the non-standard product and the practical product into corrected images that have undergone color temperature correction based on the measurement values of the first and second color temperature measurement sensors. A color conversion correction unit;
The color / texture management system according to claim 3, further comprising: The first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from non-standard products. A storage step of acquiring and storing the three-band visual sensitivity images S1i, S2i, S3i;
The coordinates corresponding to the color space of the XYZ color system are stored from the non-standard three-band visual sensitivity images S1i, S2i, S3i, the coordinates are partitioned by grids, and the number of pixels of the inspection surface belonging to each grid is determined. A non-standard product distribution calculation step of creating a color space histogram distribution of the XYZ color system by integrating and storing the color space histogram distribution and product-specific information corresponding to the database in association with each other;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, and S3i are acquired, coordinates corresponding to the color space of the XYZ color system are stored from the three-band visual sensitivity images S1i, S2i, and S3i of the above-described product, and the coordinates are partitioned by a grid. Then, by integrating the number of pixels of the inspection surface belonging to each grid, to create a color space histogram distribution of the XYZ color system, and to store the color space histogram distribution, an implementation product distribution calculation step,
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
An output step of identifying and outputting the product-specific information of a non-standard product whose color / texture approximates the implemented product with reference to the texture distribution index,
A color / texture management method, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet. The first imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from non-standard products. Three-band visual sensitivity images S1i, S2i, S3i are acquired, and the XYZ values of the pixels of the three-band visual sensitivity images S1i, S2i, S3i from the non-standard three-band visual sensitivity images S1i, S2i, S3i, or Storing a xy value normalized from the XYZ value;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution calculation step of storing a color histogram histogram three-dimensional distribution or a Lab chromaticity histogram three-dimensional distribution in the management server database in association with the product-specific information corresponding to the color space histogram distribution;
The second imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities from the actual product. Band visual sensitivity images S1i, S2i, S3i are acquired, and the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram are partitioned by a grid, and the number of pixels of the nonstandard product belonging to each grid is integrated. A product distribution calculation step for calculating the xy chromaticity histogram distribution, the XYZ chromaticity histogram three-dimensional distribution, or the Lab chromaticity histogram three-dimensional distribution created by
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
An output step of identifying and outputting the product-specific information of a non-standard product whose color / texture approximates the implemented product with reference to the texture distribution index,
A color / texture management method, wherein data communication of the storage unit, non-standard product distribution calculation unit, implementation product distribution calculation unit, color / texture calculation unit, and output unit is performed via the Internet. An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from non-standard products. A first transmission step of storing the three-band visual sensitivity images S1i, S2i, and S3i and transmitting them from the first client to the management server via the Internet;
From the non-standard three-band visual sensitivity images S1i, S2i, S3i, the XYZ values of the pixels of the three-band visual sensitivity images S1i, S2i, S3i, or the xy values normalized from the XYZ values are stored. A memory step;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid A non-standard product distribution storing step of storing the degree histogram three-dimensional distribution or the Lab chromaticity histogram three-dimensional distribution in the database of the management server in association with the product-specific information corresponding to the distribution;
An imaging unit having three spectral sensitivities (S1 (λ), S2 (λ), S3 (λ)) linearly converted equivalently to the CIE XYZ color matching function has three spectral sensitivities acquired from the product 3 A second transmission step of storing the band visual sensitivity images S1i, S2i, S3i, and transmitting them from the second client to the management server via the Internet;
xy chromaticity histogram distribution, XYZ color created by dividing the xy coordinates of the xy chromaticity diagram or the XYZ coordinates of the XYZ chromaticity diagram by a grid and integrating the number of pixels of the nonstandard product belonging to each grid An implementation product storage step of storing the degree histogram three-dimensional distribution or the Lab chromaticity histogram three-dimensional distribution in the database of the management server;
A texture distribution index calculation step of comparing and determining the distribution of the non-standard product and the distribution of the implementation product and calculating a texture distribution index;
Referring to the texture distribution index, specifying the product-specific information of a non-standard product whose color / texture approximates the implemented product, and outputting from the first client;
A color / texture management method characterized by comprising:     Instead of calculating the distribution at the management server, the first client and the second client may use the three-band visual sensitivity images S1i, S2i, S3i and the distribution calculated from the images as Internet The color / texture management method according to claim 10, further comprising a step of transmitting the data to the management server via the network. The non-standard product storage step includes:
A tag information storing step of attaching a tag to the non-standard product, and storing the distribution of the non-standard product and the product-specific information in the tag;
The output step comprises:
A tag output step of comparing and determining the distribution of non-standard products stored in the tag and the distribution of the implementation products, and outputting product-specific information of the non-standard products whose color / texture approximates the implementation products; The color / texture management method according to claim 10 or 11. The first sending step
A color temperature measurement step of measuring the color temperature by the first color temperature measurement sensor;
The second sending step
A color temperature measuring step of measuring a color temperature by a second color temperature measuring sensor;
Illumination that converts the three-band visual sensitivity images S1i, S2i, and S3i of the non-standard product and the practical product into corrected images that have undergone color temperature correction based on the measurement values of the first and second color temperature measurement sensors. A color conversion correction step;
13. The color / texture management method according to claim 10, further comprising: