# US 2019/0195624 A1 · Apparatus and method for evaluating metal surface texture

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- Number: US 2019/0195624 A1
- Document ID: US20190195624A1
- Status: Published application
- Kind: A1
- Application number: 15/854,829
- Filed: 2017-12-27
- Published: 2019-06-27
- Earliest claimed: 2017-12-27
- Assignees: Papalab Co., Ltd.
- Applicants: PaPaLaB Co., Ltd.
- Inventors: Makoto KATO
- Web page: https://patentgrep.com/patent/US20190195624A1

## CPC

- G01B 11/30: for measuring roughness or irregularity of surfaces
- G01J 3/0208: {using focussing or collimating elements, e.g. lenses or mirrors; performing aberration correction}
- G01J 3/0235: {using means for replacing an element by another, for replacing a filter or a grating}
- G01J 3/027: {Control of working procedures of a spectrometer; Failure detection; Bandwidth calculation}
- G01J 3/2803: {using photoelectric array detector}
- G01J 3/32: Investigating bands of a spectrum in sequence by a single detector
- G01J 3/36: Investigating two or more bands of a spectrum by separate detectors
- G01J 3/46: Measurement of colour; Colour measuring devices, e.g. colorimeters (measuring colour temperature G01J5/60)
- G01J 3/462: {Computing operations in or between colour spaces; Colour management systems}
- G01J 3/465: {taking into account the colour perception of the eye; using tristimulus detection}
- G01J 3/504: {Goniometric colour measurements, for example measurements of metallic or flake based paints}
- G01J 3/51: using colour filters
- G01J 3/513: {having fixed filter-detector pairs}
- G01J 3/52: using colour charts
- G01N 33/20: Metals
- G06F 18/22: Matching criteria, e.g. proximity measures
- G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
- G06V 10/56: relating to colour
- G01J 2003/1213: {Filters in general, e.g. dichroic, band}
- G01J 2003/466: {Coded colour; Recognition of predetermined colour; Determining proximity to predetermined colour}
- G01N 21/57: Measuring gloss
- G06V 10/758: {Involving statistics of pixels or of feature values, e.g. histogram matching}
- G01B: MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01J: MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY (light sources F21, H01J, H01K, H05B; investigating properties of materials by optical means G01N)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

An object is to quantify the texture such as irregularity and gloss of a metal surface. Centers of Lab chromaticity distributions are identified (S145), and one of the Lab chromaticity distribution is entirely shifted (mapped) by deviations ΔA, ΔB and ΔL of a central coordinate, such that one of central coordinates of two distributions U1(L,a,b) and U2(L,a,b) matches with the other central coordinate (S146). A texture spread index that indicates a difference in spatial spread is then computed (S147). This configuration computes the spatial spread of the Lab chromaticity distribution in a three-dimensional space, and quantifies the irregularity of an inspection plane by diffraction phenomenon of illumination light. The difference in spread other than the color is applicable to evaluation of the irregularity of the metal surface or the like.

## Claims

### Claim 1

A texture evaluation apparatus of a metal surface, comprising:
a camera configured to have three spectral sensitivities (S1(λ), S2(λ), S3(λ)) linearly and equivalently converted to a CIE XYZ color matching function;
an arithmetic processor configured to obtain and compute data by conversion of an image that has three spectral sensitivities and that is obtained by the camera into tristimulus values X, Y and Z in a CIE XYZ color system; and
a light source configured to illuminate a metal surface, wherein
the arithmetic processor is configured to:
set a specified inspection area out of data obtained by imaging the metal surface;
divide the inspection area into grids in coordinates corresponding to a color space in an XYZ color system, and integrate number of pixels included in each grid with respect to an inspection plane and a reference plane, so as to create respective color space histogram distributions in the XYZ color system; and
identify centers of the two color space histogram distributions of the inspection plane and the reference plane, and compute a texture spread index indicating a difference in spread between the color space histogram distributions by shifting the center of one of the color space histogram distributions to be closer to the other color space histogram distribution.

### Claim 2

A texture evaluation method of a metal surface using a camera configured to have three spectral sensitivities (S1(λ), S2(λ), S3(λ)) linearly and equivalently converted to a CIE XYZ color matching function, the texture evaluation method comprising:
generating data by conversion of an image that has three spectral sensitivities and that is obtained by imaging with the camera under lighting into tristimulus values X, Y and Z in a CIE XYZ color system;
setting a specified inspection area out of data obtained by imaging a metal surface;
dividing the inspection area into grids in coordinates corresponding to a color space in an XYZ color system, and integrating number of pixels included in each grid with respect to an inspection plane and a reference plane, so as to create respective color space histogram distributions in the XYZ color system; and
identifying centers of the two color space histogram distributions of the inspection plane and the reference plane, and computing a texture spread index indicating a difference in spread between the color space histogram distributions by shifting the center of one of the color space histogram distributions to be closer to the other color space histogram distribution.