U.S. patent application number 15/854829 was filed with the patent office on 2019-06-27 for apparatus and method for evaluating metal surface texture.
This patent application is currently assigned to PaPaLaB Co., Ltd.. The applicant listed for this patent is PaPaLaB Co., Ltd.. Invention is credited to Makoto KATO.
Application Number | 20190195624 15/854829 |
Document ID | / |
Family ID | 66949510 |
Filed Date | 2019-06-27 |
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United States Patent
Application |
20190195624 |
Kind Code |
A1 |
KATO; Makoto |
June 27, 2019 |
APPARATUS AND METHOD FOR EVALUATING METAL SURFACE TEXTURE
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 .DELTA.A, .DELTA.B and
.DELTA.L of a central coordinate, such that one of central
coordinates of two distributions U.sub.1(L,a,b) and U.sub.2(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.
Inventors: |
KATO; Makoto;
(Hamamatsu-shi, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PaPaLaB Co., Ltd. |
Hamamatsu-shi |
|
JP |
|
|
Assignee: |
PaPaLaB Co., Ltd.
Hamamatsu-shi
JP
|
Family ID: |
66949510 |
Appl. No.: |
15/854829 |
Filed: |
December 27, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01J 3/36 20130101; G01J
2003/1213 20130101; G06K 9/6201 20130101; G01J 3/0208 20130101;
G01J 3/0235 20130101; G01J 3/2803 20130101; G01J 3/52 20130101;
G01J 3/465 20130101; G01J 3/504 20130101; G01J 2003/466 20130101;
G01J 3/32 20130101; G01N 21/57 20130101; G01N 33/20 20130101; G01J
3/513 20130101; G01B 11/30 20130101; G01J 3/027 20130101; G01J
3/462 20130101; G01J 3/51 20130101; G06K 9/6212 20130101; G06K
9/4642 20130101; G01J 3/46 20130101; G06K 9/4652 20130101 |
International
Class: |
G01B 11/30 20060101
G01B011/30; G01J 3/46 20060101 G01J003/46; G01J 3/52 20060101
G01J003/52; G01J 3/51 20060101 G01J003/51; G01J 3/02 20060101
G01J003/02 |
Claims
1. A texture evaluation apparatus of a metal surface, comprising: a
camera configured to have three spectral sensitivities
(S1(.lamda.), S2(.lamda.), S3(.lamda.)) 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.
2. A texture evaluation method of a metal surface using a camera
configured to have three spectral sensitivities (S1(.lamda.),
S2(.lamda.), S3(.lamda.)) 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.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The contents of Japanese Appl. No. 2016-131126 filed Jun.
30, 2016 are incorporated herein by reference. Inquiries from the
public to applicants or assignees concerning this document or the
related applications should be directed to: Matthias Scholl P.C.,
Attn.: Dr. Matthias Scholl Esq., 245 First Street, 18th Floor,
Cambridge, Mass. 02142.
TECHNICAL FIELD
[0002] The present disclosure relates to a texture evaluation
apparatus and a texture evaluation method of metal surface and more
specifically relates to an evaluation apparatus configured to
evaluate texture, such as gloss, unevenness, cloudiness,
irregularity and the like, of metal surface as well as an
evaluation method.
BACKGROUND
[0003] Various methods have been proposed to polish the fine
irregularity on a metal surface
(http://www.chemicoat.co.jp/column/detail_6.html).
[0004] "Chemical polishing" denotes a polishing technique that
soaks a metal in a solution for polishing, in order to corrode the
surface of the metal by chemical reaction of an acid or an alkali.
The solution enters even a small part to process the small part,
which is not processible by mechanical polishing. Chemical
polishing is mainly suitable to polish a micro-component of a
complicated shape or an internal surface.
[0005] "Electrolytic polishing" denotes a polishing technique that
soaks a metal in a solution for electrolytic polishing and polishes
the metal as the positive electrode in the solution by the flow of
electric current. The metal is gradually electrolyzed to give a
gloss or a smooth surface. This technique does not make any altered
layer on the surface by processing and does not leave any stain or
burn. Electrolytic polishing as well as chemical polishing is used
for, for example, precision components that require a clean
polishing technique.
[0006] "Mechanical Polishing" denotes a polishing technique using a
machine and includes various methods. A rotary polishing technique
is a currently dominant technique that applies a polishing pad on a
disk-shaped surface plate, drops down a liquid polishing agent
including a chemical component and fine particles on the polishing
pad, and polishes the surface with the rotating polishing pad.
Other polishing techniques include a method of precisely polishing
an inner diameter of a metal with a honing machine and buffing
using a buff.
[0007] Satin finish
(http://www.chemicoat.co.jp/knowledge/detail_169.htmol) is a
surface treatment method that forms fine concaves and convexes on
the surface of a metal to provide rough texture. Satin finish is
also called pear-skin finish, since the finished surface is similar
to the surface of a fruit, pear. Satin finish includes gloss
finish, semi-gloss finish and matte finish. Satin finish is
employed for the purpose of improving the texture and the
appearance, for the purpose of anti-slip to help the grasp of an
oily hand, and for the purpose of pretreatment of coating or
anodization. "Manual scraping" may be employed to form fine
concaves and convexes, in order to reduce the friction resistance
of the metal surface of, for example, a precision machine.
[0008] Satin finish of the metal surface mainly includes mechanical
methods and chemical methods. The mechanical methods include a wire
brushing method that polishes the metal surface with a wire brush,
a sand blast method that sprays fine particles against the surface
by the compressed air, and a liquid honing method that sprays a
processing solution including fine particles onto a target surface
of metal processing, in addition to the manual scraping described
above. The chemical methods of satin finish include a method of
chemical corrosion or galvanic corrosion such as etching and a
surface treatment method using electroplating such as dispersal
plating.
[0009] Roughness meters are generally used for measurement of the
surface gloss or roughness. The roughness meters include mechanical
types and optical types using, for example, laser. The roughness
meter provides a numerical indication of the irregularity in the
unit of millimeter or in the unit of nanometer. A skilled worker in
the field evaluates the surface condition by visually checking the
gloss and the shine of the surface, rather than the numerical
measurement. The work generally proceeds, based on the result of
evaluation by the skilled worker in the field.
CITATION LIST
Patent Literature
[0010] Patent Literature 1: JP H07-280541A [0011] Patent Literature
2: JP H08-43060A [0012] Patent Literature 3: JP H10-227626A [0013]
Patent Literature 4: JP 2014-187558A
[0014] The techniques disclosed in Patent Literatures 1, 2 and 3
have been proposed for optical measurement of the surface.
[0015] The technique disclosed in Patent Literature 4 has been
proposed for adjustment of the color and the space. This proposed
technique uses a camera configured to have three spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) linearly and
equivalently converted to a CIE XYZ color matching function; an
arithmetic processor configured to obtain and compute coloring 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 lighting unit configured to
illuminate a measurement object. The arithmetic processor is
configured to: set a specified inspection area in the coloring data
obtained by imaging the measurement object; compute x and y values
of the inspection area normalized from X, Y and Z values of each
pixel in the inspection area with regard to an inspection object
and a reference object as the measurement objects; divide the
inspection area into grids in xy coordinates of an xy chromaticity
diagram and integrate number of pixels included in each grid with
respect to the inspection object and the reference object, so as to
create respective xy chromaticity coordinate histogram
distributions or integrate number of pixels in an XYZ chromaticity
coordinate histogram obtained in three-dimensional XYZ coordinates,
so as to create an XYZ chromaticity coordinate histogram
distribution; and compute Lab average values and a spread
difference indicating an overlap degree of the two xy chromaticity
coordinate histogram distributions or the two XYZ chromaticity
coordinate histogram distributions of the inspection object and the
reference object, so as to inspect color and texture.
[0016] This technique totally inspects the color and the texture
and is accordingly correlated to the human visual recognition. This
technique is employed, for example, in an in-line camera device for
industrial products. In the flow of industrial products, this
technique is used to detect, in real time, a color difference of
the entire industrial product or a color difference on the boundary
between parts of the industrial product like the human visual
recognition. This technique accordingly excludes coloring failures
based on the criterion similar to that of the human visual
recognition.
SUMMARY OF INVENTION
Technical Problem
[0017] The techniques disclosed in Patent Literatures 1 to 3,
however, fail to evaluate the texture indicating the finishing
quality, such as gloss, glaze, roughness and the like. The
technique disclosed in Patent Literature 4 uses a color consistency
index as a parameter for totally adjusting the color and the
texture. This is convenient for in-line color inspection but may
not be suitably used for evaluation of the texture indicating the
finishing quality of the metal surface.
[0018] Finishing the metal surface may be, for example, rough
finishing or mirror finishing. The expert's visual evaluation is
required to determine the degree of roughness. The rough finishing
of the metal surface has irregularity in the micrometer order, and
the mirror finishing of the metal surface has irregularity in the
nanometer order. The human visual evaluation in the micrometer
order and in the nanometer order takes both the time and the cost
and moreover has difficulty in accurate determination.
[0019] An object of the present disclosure is to quantify the
finishing quality of a metal surface under conditions close to
conditions of human visual evaluation by measuring a reflected
light distribution of irregularity of the metal surface that is
irradiated with light from a light source, with an XYZ system
camera and observing the light from the light source as a spread of
color by diffraction according to the configuration of the metal
surface.
Solution to Problem
[0020] The present disclosure provides evaluation using an XYZ
system camera and allows for evaluation that is significantly close
to the human visual recognition. This is because the camera
two-dimensionally or three-dimensionally provides accurate values.
The texture such as irregularity of the inspection plane may be
quantified by the diffraction phenomenon of illumination light.
[0021] By taking into account the above problem, the present
disclosure is based on the findings that in measurement of a
reflected light distribution of a metal surface illuminated with a
light source using an XYZ system camera of the present disclosure,
the light from the light source is observed by diffraction as a
spread of color and texture according to the configuration of the
metal surface, and the color and texture such as gloss, glaze,
unevenness, cloudiness, irregularity and the like of the metal
surface are quantified by this spread. According to one aspect of
the present disclosure, there is provided a texture evaluation
apparatus of a metal surface, including: a camera configured to
have three spectral sensitivities (S1(.lamda.), S2(.lamda.),
S3(.lamda.)) 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.
[0022] The metal surface herein may be, for example, surface of a
cutting tool, surface of a mold or surface of plating. The texture
includes, for example, gloss, glaze, roughness and irregularity.
The evaluation means that the finishing quality such as the
irregularity, unevenness, cloudiness, roughness, glaze and gloss of
the metal surface is evaluated.
[0023] According to another aspect, there is provided a texture
evaluation method of a metal surface using a camera configured to
have three spectral sensitivities (S1(.lamda.), S2(.lamda.),
S3(.lamda.)) linearly and equivalently converted to a CIE XYZ color
matching function, the texture evaluation method including:
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.
[0024] The term "XYZ color system" is used in a broad sense
including other CIE color systems in the claims, while being used
in a narrow sense in the description other than claims. The XYZ
color system in the narrow sense is determined to cause an RGB
color space not to take any negative value by simple linear
transformation. The XYZ color system in the narrow sense is used as
the basis of other CIE color systems, for example, Yxy, XYZ, Lab
and Luv and is a concept including a two-dimensional chromaticity
diagram or a three-dimensional color space. Accordingly, the XYZ
color system in the broad sense includes the XYZ color space in the
narrow sense and other CIE color spaces developed from the XYZ
color system in the narrow sense.
[0025] The XYZ color system in the narrow sense is a color space
determined in 1931 by CIE to cause the RGB color space not to take
any negative value by simple linear transformation, simultaneously
with the RGB color system.
[0026] The xyY color system (also called Yxy color system) is
determined to express the absolute color from the XYZ color system,
since the relationship between the numerical value and the color is
not readily understandable in the XYZ color system.
[0027] The Luv color system is one of uniform color spaces
determined in 1976 by CIE. The CIE L*u*v* color space improves the
uniformity of the wavelength interval in the xy chromaticity
diagram of the XYZ color system, on the basis of the wavelength of
light and is specified by JIS Z8518 in Japan.
[0028] The Lab color system denotes a CIE L*a*b* color space and is
derived from the XYZ color system to measure a color difference due
to the difference between perception and the instrumental
measurement and is specified by JIS Z8729 in Japan.
[0029] The XYZ color space in the broad sense includes color spaces
specified by two-dimensional coordinates and color spaces specified
by three-dimensional coordinates. Typical examples of the color
space include an XYZ color space and a Lab color space. In the case
of the two-dimensional color space, for example, Yxy color space or
Luv color space, an xy chromaticity diagram (xy chromaticity value
(plane) normalized in the Yxy color space), a uv chromaticity
diagram, and a u'v' chromaticity diagram are examples of the
two-dimensional plane. This corresponds to, for example, an xy
chromaticity histogram distribution or an Luv chromaticity
histogram distribution expressed as the density of pixels of the
two-dimensional chromaticity diagram on the plane. In the case of
the three-dimensional color space, for example, the XYZ color space
of the Lab color space, an XYZ color space and a Lab color space
are examples of the three-dimensional space. This corresponds to,
for example, an XYZ color space histogram distribution or a Lab
color space histogram distribution expressed as the density of
pixels on the three-dimensional color space.
[0030] The xy chromaticity plane, the uv chromaticity diagram and
the u'v' chromaticity diagram provide two-dimensional separation of
texture from color, while the other XYZ color spaces, the Lab color
space and the like provide three-dimensional separation of texture
from color. Accordingly, the terms of XYZ color space histogram and
Lab color space histogram are defined distinctively from the term
of xy chromaticity histogram.
[0031] The XYZ color space histogram and the Lab color space
histogram are different from each other. Computation of the texture
spread index in the Lab color space converts XYZ color data into
Lab chromaticity data and computes the texture spread index from
the converted data.
[0032] Any of various shifting techniques may be employed to bring
one histogram distribution close to the other histogram
distribution; for example, a technique of causing a predetermined
position, for example, the center of one histogram distribution to
match with the center of the other histogram distribution, a
technique of bringing predetermined positions, for example, the
respective centers of histogram distributions close to each other
in a predetermined range, a technique of moving one histogram
distribution in parallel to a coordinate axis to be close to the
other histogram distribution, or a technique of linearly moving one
center to be close to the other center. The amount of shift, the
direction of shift and the like may be set appropriately, as long
as such a shift provides an appropriate index.
[0033] The index is not limited to a two-dimensional form or a
three-dimensional form but may be any of various forms, such as an
index number, a graph, a graphic or any combination thereof.
[0034] According to one technically related aspect of the
disclosure, there is provided a texture evaluation apparatus of a
metal surface, comprising: a camera configured to have three
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.))
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 lighting unit
configured to illuminate a metal surface. The arithmetic processor
is configured to: set a specified inspection area out of data
obtained by imaging the metal surface; calculate an X value, a Y
value and a Z value of each of pixels included in the inspection
area with respect to an inspection plane and a reference plane as
the metal surface, divide the inspection area into grids in xy
coordinates, XYZ coordinates or Lab coordinates that respectively
correspond to an xy chromaticity diagram, an XYZ chromaticity
diagram or a Lab chromaticity diagram, and integrate number of
pixels included in each grid with respect to the inspection plane
and the reference plane, so as to create xy chromaticity histogram
distributions, XYZ color space histogram distributions or Lab color
space histogram distributions; and identify centers of the two xy
chromaticity histogram distributions, the two XYZ color space
histogram distributions or the two Lab color space histogram
distributions of the inspection plane and the reference plane, and
compute a texture spread index of the xy chromaticity histogram
distribution, the XYZ color space histogram distribution or the Lab
color space histogram distribution by shifting the center of one of
the xy chromaticity histogram distributions, one of the XYZ color
space histogram distributions or one of the Lab color space
histogram distributions to be closer to the other xy chromaticity
histogram distribution, the other XYZ color space histogram
distribution or the other Lab color space histogram
distribution.
[0035] According to another technically related aspect of the
disclosure, there is provided a texture evaluation method of a
metal surface using a camera configured to have three spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) linearly and
equivalently converted to a CIE XYZ color matching function. The
texture evaluation method comprises: 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 the metal surface;
calculating an X value, a Y value and a Z value of each of pixels
included in the inspection area with respect to an inspection plane
and a reference plane as the metal surface, dividing the inspection
area into grids in xy coordinates, XYZ coordinates or Lab
coordinates that respectively correspond to an xy chromaticity
diagram, an XYZ chromaticity diagram or a Lab chromaticity diagram,
and integrating number of pixels included in each grid with respect
to the inspection plane and the reference plane, so as to create xy
chromaticity histogram distributions, XYZ color space histogram
distributions or Lab color space histogram distributions; and
identifying centers of the two xy chromaticity histogram
distributions, the two XYZ color space histogram distributions or
the two Lab color space histogram distributions of the inspection
plane and the reference plane, and computing a texture spread index
of the xy chromaticity histogram distribution, the XYZ color space
histogram distribution or the Lab color space histogram
distribution by shifting the center of one of the xy chromaticity
histogram distributions, one of the XYZ color space histogram
distributions or one of the Lab color space histogram distributions
to be closer to the other xy chromaticity histogram distribution,
the other XYZ color space histogram distribution or the other Lab
color space histogram distribution.
[0036] The imaging device of the present disclosure images the
reference plane and the inspection plane with three spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.), i.e., in
three channels. The means employed for this purpose may be any of,
for example, an optical filter, a dichromic mirror or a dichroic
prism set to obtain such spectral sensitivities.
[0037] The spectral sensitivities (S.sub.1(.lamda.),
S.sub.2(.lamda.), S.sub.3(.lamda.)) of the imaging device are
bell-shaped curves that have single peaks and do not take negative
values according to the CIE XYZ spectral characteristics, and are
obtained by equivalent conversion under the conditions that the
respective spectral sensitivity curves have an identical peak value
and a minimum overlap. The curve of spectral characteristic S.sub.1
has the peak wavelength of 582 nm, the half width of 523 to 629 nm
and the 1/10 width of 491 to 663 nm. The curve of spectral
characteristic S.sub.2 has the peak wavelength of 543 nm, the half
width of 506 to 589 nm and the 1/10 width of 464 to 632 nm. The
curve of spectral characteristic S3 has the peak wavelength of 446
nm, the half width of 423 to 478 nm and the 1/10 width of 409 to
508 nm.
Advantageous Effects
[0038] The present disclosure quantifies the texture such as gloss,
glaze, irregularity and the like of the metal surface by the
diffraction phenomenon of illumination light using an XYZ system
camera. This configuration advantageously allows for accurate and
efficient evaluation that is significantly close to the human
visual recognition.
BRIEF DESCRIPTION OF DRAWINGS
[0039] FIG. 1 is a block diagram illustrating a texture evaluation
apparatus 1 of metal surface according to Embodiment 1 of the
present disclosure;
[0040] FIG. 2 is a graph showing spectral sensitivities of a
two-dimensional colorimeter 2 that is an XYZ color system camera
according to Embodiment 1 of the present disclosure;
[0041] FIGS. 3A, 3B, and 3C are diagrams illustrating concrete
examples of method of obtaining image information with three
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.))
according to Embodiment 1 of the present disclosure: FIG. 3A is a
diagram illustrating an example using dichroic mirrors; FIG. 3B is
a diagram illustrating an example using a filter turret; and FIG.
3C is a diagram illustrating an example using optical filters 22a,
22b and 22c microscopically applied on an imaging element 23;
[0042] FIG. 4 is a flowchart showing a processing flow performed by
the two-dimensional colorimeter 2 according to Embodiment 1 of the
present disclosure;
[0043] FIG. 5 is a flowchart showing a processing flow by an
arithmetic processor 3 according to Embodiment 1 of the present
disclosure;
[0044] FIG. 6 is a sub-flowchart showing a sub-flow by the
arithmetic processor 3 according to Embodiment 1 of the present
disclosure;
[0045] FIG. 7 is a diagram illustrating a shift process in an xy
coordinate space by the arithmetic processor 3 according to
Embodiment 1 of the present disclosure;
[0046] FIG. 8A is a diagram illustrating an inspection area T in
the arithmetic processor 3 according to Embodiment 1 of the present
disclosure; FIG. 8B is an xy chromaticity diagram showing a
chromaticity area K in the chromaticity diagram corresponding to
the inspection area T; FIG. 8C is a diagram showing the
chromaticity area K divided into grids G; FIG. 8D is a diagram
showing an overlap of chromaticity in an xy two-dimensional
chromaticity diagram; FIG. 8E is a diagram showing a minimum
distribution; and FIG. 8F is a diagram showing one example of xy
chromaticity histogram distribution;
[0047] FIG. 9A is a diagram showing the metallic degrees of metal
surfaces; FIG. 9B is a diagram showing xy chromaticity histogram
distributions; and FIG. 9C is a three-dimensional image diagram
showing xy chromaticity histogram distributions;
[0048] FIG. 10 is a block diagram illustrating the configuration of
a texture evaluation apparatus 101 of metal surface according to
Embodiment 2 of the present disclosure;
[0049] FIG. 11 is a flowchart showing a processing flow (XYZ color
space distribution) performed by an arithmetic processor 103 in the
texture evaluation apparatus 101 of metal surface according to
Embodiment 2 of the present disclosure;
[0050] FIG. 12 is a flowchart showing a processing flow (Lab color
space distribution) performed by the arithmetic processor 103 in
the texture evaluation apparatus 101 of metal surface according to
Embodiment 2 of the present disclosure;
[0051] FIGS. 13A and 13B are diagrams illustrating a shift process
in an xy coordinate space performed by the texture evaluation
apparatus 101 of metal surface according to Embodiment 2 of the
present disclosure;
[0052] FIG. 14 is a block diagram illustrating the configuration of
a texture evaluation apparatus 201 of metal surface according to
Embodiment 3 of the present disclosure;
[0053] FIGS. 15A and 15B are diagrams illustrating a shift process
in a Lab coordinate space performed by the texture evaluation
apparatus 201 of metal surface according to Embodiment 3 of the
present disclosure;
[0054] FIG. 16 is photographs illustrating measurement ranges and
measurement locations (within the square frames of images) of
sample No. 2 of Example 1;
[0055] FIG. 17 is photographs illustrating measurement ranges and
measurement locations (within the square frames of images) of
sample No. 2 of Example 2;
[0056] FIG. 18 is photographs illustrating measurement ranges and
measurement locations (within the square frames of images) of
samples No. 8 and No. 4 of Example 3; and
[0057] FIG. 19 is photographs illustrating measurement ranges and
measurement locations (within the square frames of images) of
samples No. 5 to No. 7 of Example 3.
DESCRIPTION OF EMBODIMENTS
[0058] A texture evaluation apparatus 1 of metal surface according
to a preferable embodiment 1 of the present disclosure is described
below with reference to FIGS. 1 to 10.
[0059] The surface texture evaluation apparatus 1 includes a
two-dimensional colorimeter 2 configured to have three spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) linearly and
equivalently converted to a CIE XYZ color matching function, an
arithmetic processor 3 configured to obtain and compute data by
conversion of an image that has three spectral sensitivities and
that is obtained by the two-dimensional colorimeter 2 into
tristimulus values X, Y and Z in a CIE XYZ color system, and a
lighting unit 6 configured to illuminate a metal surface 5. The
arithmetic processor 3 sets a specified inspection area out of data
obtained by imaging the metal surface 5, converts X, Y and Z values
of each pixel in the inspection area into Lab values with regard to
an inspection plane and a reference plane as the metal surface 5,
and calculates respective average values of the Lab values. The
arithmetic processor 3 subsequently divides the inspection area by
grids in xy coordinates of an xy chromaticity diagram, integrates
the number of pixels included in each grid with regard to the
inspection plane and the reference plane, so as to create
respective Lab color space histogram distributions, and identifies
centers of two xy chromaticity histogram distributions of the
inspection plane and the reference plane. The arithmetic processor
3 then shifts the center of one of the two xy chromaticity
histogram distributions, an XYZ color space histogram distribution
or an Lab color space histogram distribution to match with the
center of the other xy chromaticity histogram distribution, and
computes a width difference of the xy chromaticity histogram
distribution, the XYZ color space histogram distribution or the Lab
color space histogram distribution. The metal surface may be, for
example, surface of a cutting tool, a mold or plating.
[0060] The flip-flop provides different views in different angles.
The two-dimensional colorimeter 2 is manually moved for imaging in
at least three different angles. The two-dimensional colorimeter 2
is placed below the lighting unit 6, and the angle of the
two-dimensional colorimeter 2 is manually changeable. The metal
surface 5 and its Lab color histogram distribution data are
measurable in multiple different angles by the two-dimensional
colorimeter 2.
[0061] The spectral sensitivities of the two-dimensional
colorimeter 2 satisfy Luther condition. As shown in FIG. 2, the
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) are
converted equivalently from an XYZ color matching function on the
conditions that spectral sensitivity curves take no negative
values, are bell-shaped curves with single peaks, have an identical
peak value and have a minimum overlap. More specifically the
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) have
the following characteristics:
TABLE-US-00001 Peak Wavelength Half Width 1/10 Width S1 582 nm
523-629 nm 491-663 nm S2 543 nm 506-589 nm 464-632 nm S3 446 nm
23-478 nm 409-508 nm
[0062] The peak wavelength of the spectral characteristic S1 may be
regarded as 580.+-.4 nm, the peak wavelength of the spectral
characteristic S2 may be regarded as 543.+-.3 nm, and the peak
wavelength of the spectral characteristic S3 may be regarded as
446.+-.7 nm.
[0063] The three spectral sensitivities (S1(.lamda.), S2(.lamda.),
S3(.lamda.)) are calculated according to Mathematical Expression 1
given below. Refer to, for example, JP 2005-257827A for the details
of spectral characteristics.
[ S 1 ( .lamda. ) S 2 ( .lamda. ) S 3 ( .lamda. ) ] = [ 0.51151
0.60975 - 0.10930 - 0.38668 1.16031 0.07538 0.0 0.0 0.56086 ] [ x _
( .lamda. ) y _ ( .lamda. ) z _ ( .lamda. ) ] [ Math . 1 ]
##EQU00001##
[0064] The two-dimensional colorimeter 2 may be, for example, a
two-dimensional colorimeter RC-500 manufactured by PaPaLab Co.,
Ltd. and has the specifications of the effective frequency of about
500 million pixels, the effective area of 9.93 mm.times.8.7 mm, the
image size of 3.45 .mu.m.times.3.45 .mu.m, the video output of 12
Bit, the camera interface of GigE, the number of frames (at
focusing) of 3 to 7 frames per sec, the shutter speed of 1/15,600
sec to 1/15 sec, the integration time of up to 3 seconds, the S/N
ratio of not lower than 60 dB, F mount as the lens mount, the
operation temperature of 0.degree. C. to 40.degree. C., and the
operation humidity of 20% to 80%.
[0065] As shown in FIG. 1, the two-dimensional colorimeter 2
includes a photographic lens 21, three optical filters 22a, 22b and
22c placed behind the photographic lens 21 and an imaging element
23 (for example, CCD or CMOS) placed behind the optical filters
22a, 22b and 22c. The three spectral sensitivities (S1(.lamda.),
S2(.lamda.), S3(.lamda.)) of the two-dimensional colorimeter 2 are
given as the products of spectral transmittances of the optical
filters 22a, 22b and 22c and the spectral sensitivity of the
imaging element 23. The positional relationship between the optical
filters 22a, 22b and 22c and the imaging element 23 shown in FIG. 1
is only illustrative. The following describes specific methods of
obtaining image information using the three spectral sensitivities
(S1(.lamda.), S2(.lamda.), S3(.lamda.)). Embodiment 1 may employ
any of these methods or may employ another suitable method. An
arithmetic unit 24 and a display unit 25 are also included in the
two-dimensional colorimeter 2.
[0066] FIG. 3A illustrates a method using dichroic mirrors. This
method makes light of a specified wavelength reflected by a
dichroic mirror 22c', disperses the transmitted light by making
light of another specified wavelength reflected by another dichroic
mirror 22a', and obtains information from three imaging elements
23a, 23b and 23c arrayed in parallel to one another. In this
system, the dichroic mirror 22a' corresponds to the optical filters
22a and 22b, and the dichroic mirror 22c' corresponds to the
optical filter 22c. With regard to incident light from a
photographic lens 21, light of spectral sensitivity S3 is reflected
by the dichroic mirror 22c', and the remaining light is
transmitted. The light reflected by the dichroic mirror 22c' is
reflected by a reflector 26, and the imaging element 23c provides
spectral sensitivity S3. With regard to the light transmitted
through the dichroic mirror 22c', on the other hand, light of
spectral sensitivity S1 is reflected by the dichroic mirror 22a',
and the remaining light of spectral sensitivity S2 is transmitted.
The imaging elements 23a and 23b respectively provide spectral
sensitivity S1 and spectral sensitivity S2. The dichroic mirrors
may be replaced with a dichroic prism having similar
characteristics to disperse light into three lights. Imaging
elements 23a, 23b and 23c may be applied on the dichroic prism at
positions where the respective lights are transmitted.
[0067] FIG. 3B illustrates a method using a filter turret 27.
Optical filters 22a, 22b and 22c are provided on the filter turret
27 having an axis of rotation in a direction identical with the
direction of incident light from a photographic lens 21 and are
mechanically rotated. An imaging element 23 receives the
sequentially transmitted lights and provides three spectral
sensitivities S1, S2 and S3.
[0068] FIG. 3C illustrates a method using optical filters 22a, 22b
and 22c microscopically applied on an imaging element 23. The
optical filters 22a, 22b and 22c are provided in a Bayer array on
the imaging element 23. In this array, the optical filer 22b is
arranged in half of an area on the imaging element 23 in a grid
pattern, and the optical filters 22a and 22c are arranged equally
in respective halves of the remaining area. Accordingly, the layout
ratio of the optical filter 22a: optical filter 22b: optical filter
22c=1:2:1. The array of the optical filters 22a, 22b and 22c is,
however, not necessarily limited to the Bayer array according to
Embodiment 1. The respective optical filters 22a, 22b and 22c are
microscopic and are applied on the imaging element 23 by printing.
The invention is, however, not characterized by this array but is
characterized by application of filters characteristic of spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) on an imaging
element.
[0069] The two-dimensional colorimeter 2 sends image information
obtained with the spectral sensitivities (S1(.lamda.), S2(.lamda.),
S3(.lamda.)) to the arithmetic processor 3. The arithmetic
processor 3 converts the image information into tristimulus values
X, Y and Z in the XYZ color system and performs an arithmetic
operation by conversion of the obtained image data of the
tristimulus values X, Y and Z. The arithmetic processor 3 includes
a display unit (not shown) configured to display a visualized
image.
[0070] The arithmetic processor 3 computes and visualizes the
luminance, the chromaticity and the like at any position in the
image obtained by the two-dimensional colorimeter 2. The metal
surface 5 is obliquely irradiated with light. The arithmetic
processor 3 compares and indexes the xy, the XYZ or the Lab
chromaticity distribution data of the metal color.
[0071] The two-dimensional colorimeter 2 generally images the metal
surface 5 at one location, and may be moved to image the metal
surface 5 at a different angle as needed basis. For example, the
metal surface 5 may be imaged at three different locations (any
adequate number of locations) such as at the front, 45 degrees left
and 45 degrees right.
[0072] A xenon lamp (simulated solar light) is employed as the
light source of the lighting unit 6. The lighting unit 6 includes a
Fresnel lens assembly, in addition to the xenon lamp. The metal
surface 5 is uniformly irradiated obliquely downward with the xenon
lamp. The xenon lamp may be replaced with an LED artificial
sunlight lamp. The natural solar light may be used as a lighting
source.
[0073] A display unit 7 is connected with the arithmetic processor
3 and is configured to receive an image signal processed by the
arithmetic processor 3 and display an image on the screen. The
arithmetic processor 3 or the display unit 7 adequately includes an
input unit (not shown). The input unit may be, for example, a
keyboard, a mouse or a touch panel provided on an image display
device.
[0074] The following describes the operations of the texture
evaluation apparatus 1 of the metal surface 5 with reference to a
concrete example. As shown in FIG. 1, the texture evaluation
apparatus 1 of the metal surface 5 is operated by connection of the
two-dimensional colorimeter 2, the arithmetic processor 3 and the
display unit 7. The method of connection may be freely selectable
among various wired and wireless connection techniques. FIG. 4 is a
flowchart showing a processing flow performed by the
two-dimensional colorimeter 2, and FIG. 6 is a flowchart showing a
processing flow performed by the arithmetic processor 3.
[0075] As shown in FIG. 4, when being powered on, the
two-dimensional colorimeter 2 is initialized (initialization S1).
The two-dimensional colorimeter 2 takes an image of the metal
surface 5 with the spectral sensitivities (S1(.lamda.),
S2(.lamda.), S3(.lamda.)) (imaging process S2), subsequently inputs
the taken image data by means of the imaging element 23 (input
process S3), and converts the input data into tristimulus values X,
Y and Z by the arithmetic processor 3 (conversion process S4). The
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) are
sent to the display unit 7 (data transmission S5). When the image
is a moving image, the series of processing from the imaging
process S2 to the data transmission S5 is performed continuously.
The image is displayed on the image display unit 7.
[0076] In the imaging process S2, any of various metal surfaces 5
may be measured. The metal surface 5 is imaged with the
two-dimensional colorimeter 2 at different angles with regard to a
specified area in different imaging locations. There are a
plurality of imaging locations, and any adequate number of imaging
locations may be selected. For example, the metal surface 5 is
measured in three different directions, i.e., at the front (0
degree), 45 degrees left and 45 degrees right. At the measurement
location, the optical axis of the two-dimensional colorimeter 2 at
0 degree is perpendicular to the metal surface 5. Lighting is
characterized by obliquely downward lighting like the sunlight.
[0077] For the purpose of reference, conversion equations from the
tristimulus values X, Y and Z into a Y'xy color system are given as
Mathematical Expressions 2 and 3. A luminance meter (not shown) is
used together with the two-dimensional colorimeter 2, and the value
Y is corrected with a value (nt) of the luminance meter to Y'. The
conversion equations in the color space are commonly used, so that
the other equations are not specifically shown.
[0078] The XYZ color system is currently used as a CIE standard
color system and is the basis of the respective color spaces. The
XYZ color system is developed based on the principle of additive
mixture of three primary colors of light (R=red, G=green and
B=blue) and expresses each color by three values Y, x and y using
the chromaticity diagram. Y denotes the reflectance and corresponds
to the brightness, and x and y denote chromaticities.
x = X X + Y + Z [ Math . 2 ] y = Y X + Y + Z [ Math . 3 ]
##EQU00002##
[0079] The imaging process S2 is a process of imaging the metal
surface 5 with the two-dimensional colorimeter 2 having the three
spectral sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) (as
shown in FIGS. 1 and 4). The spectral sensitivities (S1(.lamda.),
S2(.lamda.), S3(.lamda.)) are given by above Mathematical
Expression 1. The input process S3 is concurrently and sequentially
performed with imaging using the photographic lens 21, the optical
filters 22a, 22b and 22c and the imaging element 23.
[0080] The input image data are values according to the spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)). The
arithmetic processor 3 performs the conversion process S4 to
convert the image data of the image taken by the two-dimensional
colorimeter 2 into tristimulus values X, Y and Z. This conversion
is performed according to Mathematical Expression 1. More
specifically, tristimulus values X, Y and Z are obtained by
multiplication of an inverse matrix of the coefficients in
Mathematical Expression 1. The two-dimensional colorimeter 2 sends
the values according to the spectral sensitivities (S1(.lamda.),
S2(.lamda.), S3(.lamda.)) to the arithmetic processor 3.
[0081] As shown in FIG. 5, when being powered on, the arithmetic
processor 3 is initialized (initialization S110). In the state that
the display unit 7 is connected with the two-dimensional
colorimeter 2, the arithmetic processor 3 receives the spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) sent from the
two-dimensional colorimeter 2 (data receiving S120). The arithmetic
processor 3 subsequently converts the spectral sensitivities
(S1(.lamda.), S2(.lamda.), S3(.lamda.)) into tristimulus values X,
Y and Z (S140) and sends the result of computation to the display
unit 7 (display process S150). In response to the data receiving
S120 from the two-dimensional colorimeter 2, the series of
processing from the conversion process S130 to the display process
S150 is performed continuously.
[0082] The computation process S140 is a process of computing and
visualizing Lab average values and xy texture spread indexes of the
taken images with regard to the reference plane and the inspection
plane. Color information may be converted into, for example, RGB
data as required for display on the display unit 7.
[0083] The display process S150 is a process of displaying the
visualized texture spread indexes on the image display device. The
processing flow then goes to return.
[0084] The following describes the details of the process S140 with
reference to a sub-flowchart of FIG. 6. The process takes a first
image (image B) of the reference plane, takes a second image (image
A) of the inspection plane to be compared, and sequentially
calculates the texture spread indexes as described below. The
texture spread indexes that separate the texture are used to
identify the similarity of texture.
[0085] The process sets an inspection area K (shown in FIG. 8B)
corresponding to a target area T to be inspected (shown in FIG. 8A)
with regard to obtained images A and B (step S141). The size and
the location of the inspection area K may be set arbitrarily.
[0086] The process computes the chromaticities xy and determines
the chromaticities Yxy (S142).
[0087] The process creates an xy chromaticity histogram
distribution of the inspection plane in the area K cut out from the
taken image A of the inspection plane (S143). This chromaticity
histogram distribution shows an integrated number of pixels
included in an overlap area D of two histogram distributions shown
in FIG. 8D.
[0088] The xy chromaticity histogram distribution is a
three-dimensional histogram showing an integrated number of pixels
included in each of the unit grids described above, and the overlap
area D is shown in FIG. 8D.
[0089] FIG. 8C shows a plane of color distribution for comparison
at the positions of xy coordinates. The inspection area K is
divided into grids G, and a histogram distribution is created by
integrating the number of pixels having the xy values in each
divisional area with respect to the z axis. Each divisional area is
a plane grid by dividing the xy coordinates in grids of a specific
width (three-dimensional grids), for example, by dividing xy
coordinates into 1000 (by 1000 lines). The histogram is scanned
from one end to the other end. The number of pixels included in
each divisional area divided as the grid G is scanned in an
identical xy plane and is integrated in the z direction. Computing
the xy coordinates in only a specified range with regard to the
inspection area K shortens the computation time. Using the smaller
grids increases the accuracy but also increases the computation
time. Accordingly, grids of adequate size are to be used.
[0090] Like S143, the process creates an xy chromaticity histogram
distribution with regard to the image B of the reference plane
(S144). In the xy chromaticity histogram distribution, the xy axes
shows xy chromaticities and the z axis shows the integrated number
of pixels. FIG. 8D shows an overlap area in two dimensions.
[0091] The process individually sums the values of the L axis, the
a axis and the b axis in the Lab space with regard to all the
pixels included in the inspection area and divides the respective
sums of the L value, the a value and the b value by the total
number of pixels. This calculates an average L value, an average a
value and an average b value in the Lab chromaticity distribution
(S145).
[0092] The process calculates the Lab values in the Lab space
converted by Mathematical Expression 4 given below. The Lab color
space is a type of complementary color space and has a dimension L
representing the brightness and complementary color dimensions A
and B. This is based on the nonlinearly compressed coordinates in a
CIE XYZ color space. The XYZ values prior to normalization are
converted into Lab values by Mathematical Expression 4. This
provides a distribution in the Lab color space by addition in the
brightness direction to the distribution in the XYZ color
space.
f ( t ) = { t 1 / 3 When t > ( 6 / 29 ) 3 = 0.008856 [ ( 29 / 3
) 3 t + 16 ] / 116 Otherwise [ Math . 4 ] L * = 116 f ( Y / Y n ) -
16 a * = 500 [ f ( X / X n ) - f ( Y / Y n ) ] b * = 200 [ f ( Y /
Y n ) - f ( Z / Z n ) ] ##EQU00003##
[0093] In Mathematical Expression 4, the values X, Y and Z are
respectively divided by coordinates Xn, Yn and Zn of a neutral
point in the parentheses of the function f, in order to adjust
their maximum values to 1.
[0094] Differences of the average values of the reference plane and
the inspection plane are calculated and are used as the criteria
for determination of color difference.
[0095] The process identifies central coordinates C1 and C2 in the
xy chromaticity distribution (S146) as shown in FIG. 7. The central
coordinate herein denotes a centroid (center of gravity).
[0096] The process shifts (maps) the entire xy chromaticity
distribution by a deviation .DELTA.F of the central coordinate,
such that one of central coordinates of two xy histogram
distributions H.sub.1(x,y) and H.sub.1(x,y) matches with the other
central coordinate (S147) as shown in FIG. 7. A difference in color
component is calculated without such a shift of one distribution to
the other distribution. The distribution may be shifted on the
graph or may be shifted by calculation. The amount of the shift may
be set appropriately. A shift of one center into a predetermined
range around the other center has similar effects as those of a
shift of one center to the other center. There is accordingly a
need to bring the two distributions closer to each other by an
appropriate amount of shift that enables the texture to be
evaluated.
[0097] The process computes a texture spread index that indicates a
spatial spread difference (S148). This computation simply extracts
a difference in metallic degree, separates the metallic degree from
the similarity of chromaticity, and determines and quantifies the
metallic degree. This computation computes the spread in the
two-dimensional space of xy chromaticity distribution and
recognizes a difference in spread as a difference in glitter
between glitter materials excluding the color. This accordingly
enables the texture to be accurately detected separately from the
color.
[0098] The texture spread index is calculated by an expression
given below. The xy chromaticity histogram distribution indicates
an integrated number of pixels. FIG. 8D shows an overlap area D,
and FIG. 8E shows a minimum distribution.
texture spread index=integrated number of pixels included in
overlap area D/total number of pixels in inspection area
K.times.100(%)
[0099] The process calculates spread histograms of the reference
plane and the inspection plane on the two-dimensional space and
takes a minimum value in the spread histograms at an identical
position as an overlap frequency. The overlap frequency is divided
by the total number in the entire histogram.
[0100] FIGS. 8D and 8E show one cross section taken on an S-S line
of FIG. 8C. There is an overlap in the xy coordinates on the same
line. As a matter of convenience, the cross section is shown in two
dimensions, instead of three dimensions. The histogram has a
distribution in a small step-like shape. An integrated number
H.sub.1 and an integrated number H.sub.2 in FIG. 8D respectively
correspond to the image A and the image B. Comparison of the two
histogram distributions provides an overlap area D.
[0101] In FIG. 8E, H.sub.1(x.sub.1,y.sub.1) denotes an integrated
number of the xy chromaticity histogram distribution of the
inspection plane, and H.sub.2(x.sub.1,y.sub.1) denotes an
integrated number of the xy chromaticity histogram distribution of
the inspection plane. In a left side area of the overlap area,
H.sub.1>H.sub.2. In the overlap area, H.sub.1=H.sub.2. In a
right side area, H.sub.1<H.sub.2. The smaller integrated number
(pixel frequency) between H.sub.1 and H.sub.2 is H.sub.1 in the
left side area and H.sub.2 in the right side area. This provides a
minimum distribution that is a step-like histogram curve. The ratio
of the overlap area D to the overall area may be calculated by
using this minimum distribution.
[0102] The smaller integrated number is specified in this minimum
distribution. The integrated number in the overlap area D is
calculated by summing up the smaller integrated number between
H.sub.1 and H.sub.2. The ratio to the total number of pixels is
then determined. The total number of pixels in the inspection area
K is fixed. The inspection plane and the reference plane have an
identical total number of pixels. This ratio may be integrated
three-dimensionally with regard to all the grids G. In another
example, as shown in FIG. 8C, the distribution of the integrated
number of pixels may be integrated two-dimensionally, for example,
by cutting the inspection area K along the S-S line and changing
the value x from one end to the other end while fixing the value y
to a predetermined value. FIG. 8F is a two-dimensional map showing
the results of integration on the xy coordinates. The inspection
area K having no distribution and having the pixel number of zero
is excluded from the computation.
[0103] The process then performs the display and storage process
and the transmission process (S149) and goes to Return.
[0104] For example, it is assumed that the number of pixels
included in the inspection area K is 100 pixels in length.times.100
pixels in width=10,000 pixels. Corresponding inspection areas K are
cut from the respective images, so that both the image A and the
image B have the same total number of pixels, i.e., 10,000 pixels.
The number of pixels in the overlap area is integrated from the xy
chromaticity histogram. The integrated number of 5,000 indicates
the texture spread index of 50%. The degree of difference in
texture increases with a decrease in texture spread index from
100%. The texture spread index of 100% indicates the complete
consistency of the distribution of the xy values. The texture
spread index of or above a predetermined value is evaluated as the
plane of texture conformance.
[0105] The color information obtained primarily from an image is
three spectral sensitivities (S1(.lamda.), S2(.lamda.),
S3(.lamda.)) by a function equivalent to an XYZ color matching
function. Compared with color information in RGB, this color
information is more faithful to the sensitivity of human eye and
the higher accuracy. This provides a small overlap of the spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) and a
sufficiently high S/N ratio, and natural changes in curves of
spectral sensitivities. This accordingly minimizes the error in
colorimetry.
[0106] The texture of the image may be recognized separately from
the color by the histogram distribution. Even the subtle color
difference is thus determinable by reflecting the differences of
gloss, glaze, irregularity, roughness and the like of the
surface.
[0107] The following describes an example of inspecting three
different planes having different levels of metallic texture with
reference to FIGS. 9A to 9C. A plane having the lowest level of
metallic texture is specified as a reference plane 1, a plane
having the medium level of metallic texture is specified as an
inspection plane 2, and a plane having the highest level of
metallic texture is specified as an inspection plane 3.
Distributions of the respective planes 1 to 3 that are subjected to
the above series of processing are created in the xy chromaticity
diagram. As shown in the xy chromaticity diagram of FIG. 9B, these
distributions are given as data of integration of the highlighted
areas. The integrated number is expressed by brightness. The
brighter color indicates the larger integrated number. FIG. 9C
schematically illustrates the integrated numbers of the reference
plane and the inspection planes in three dimensions. The xy axes
show the chromaticities, and the z axis shows the integrated
number. The higher metallic texture basically provides a lower and
wider peak, and the lower metallic texture provides a sharper peak.
A texture spread index indicating an overlap degree is computed by
comparing two histograms of the reference plane 1 and the
inspection plane 2 or 3.
[0108] As shown in Table 1, a comparative example uses Lab values
calculated as average values of color having .DELTA.E as the basis
of texture. This provides only small differences in Lab values and
.DELTA.E compared with the visual recognition, and thereby leads to
a difficulty in inspection. The texture spread index of this
embodiment, however, directly uses an integrated number in the
range of the inspection area K. The texture spread indexes of the
inspection plane 2 and the inspection plane 3 relative to the
reference plane 1 are respectively 80% and 30%. This allows for the
clear and easy numerical discrimination of the metallic
texture.
TABLE-US-00002 TABLE 1 Comparative inspection example of reference
plane (1) and inspection plane (2), (3) Texture spread L value a
value b value .DELTA.E index (1) 50.40 -1.96 10.60 -- -- (2) 50.52
-1.97 10.21 0.121 (3) 51.13 -1.96 10.45 0.740 30%
[0109] A texture evaluation apparatus 101 of a metal surface 105
according to Embodiment 2 is described below with reference to FIG.
10, FIG. 11 and FIG. 12. Like elements are expressed by like
numerals in 100s and are not specifically described. The following
mainly describes the differences.
[0110] The texture evaluation apparatus 101 includes a
two-dimensional colorimeter 102 configured to take images of a
reference plane and an inspection plane, an arithmetic processor
103 connected with the two-dimensional colorimeter 102 via a switch
106 and configured to receive signals and compute a texture spread
index, and a display unit 107 connected with the arithmetic
processor 103 and configured to display the index.
[0111] As shown in FIG. 10, the arithmetic processor 103 includes
an operator 103A configured to compute stimulus values XYZ1
obtained by imaging the color sample as a reference plane, an
operator 103B configured to compute stimulus values XYZ2 obtained
by imaging a color sample as an inspection plane, and an operator
103C connected with the operator 103A and the operator 103B and
configured to compute a texture spread index of the metal surface
105. An OK signal or an NG signal from the operator 103C is sent to
the display unit 107 or is sent to the outside. The switch 106 is
configured to selectively input the stimulus values XYZ1 or the
stimulus values XYZ2.
[0112] FIG. 11 is a flowchart showing a process of computing the
texture spread index by comparison of chromaticity histogram
distributions of two images A and B. As shown in FIG. 11, when the
program is triggered, the process extracts, specifies and sets an
inspection area K from the image A (S201). The process subsequently
extracts, specifies and sets an inspection area corresponding to
that of the image A from the image B (S202). The process computes
chromaticity values XYZ from the images A and B (S203). The process
respectively computes and creates XYZ color space histogram
distributions of the inspection plane and the reference plane with
regard to the inspection area K (S204). The process subsequently
calculates average values of the XYZ values (S205), identifies a
central coordinate of an XYZ color space distribution (S206), and
shifts the XYZ color space distribution to the central coordinate
(S207). After the shift process, the process identifies a central
coordinate of the XYZ color space distribution (S208). This aims to
check the suitability of the central coordinate after the shift
process. The central coordinate may be readjusted as needed. The
process subsequently identifies a minimum distribution of the XYZ
color space histogram distributions and computes an integrated
number in an overlap area D of the XYZ color space histogram
distributions (S209). The texture spread index is given as
(integrated number of pixels included in the overlap area D/total
number of pixels in the inspection area K).times.100(%). The
smaller integrated number between T.sub.1 and T.sub.2 is summed up
as the integrated number in the overlap area D. The process
computes the texture spread index (S210) and goes to Return.
[0113] In computation of an XYZ distribution corresponding to the
inspection area K, the index is computed, based on distributions of
an X axis, a Y axis and a Z axis in a three-dimensional space. As
shown in FIGS. 13A and 13B, T.sub.1(X,Y,Z) and T.sub.2(X,Y,Z)
respectively denote XYZ values of the inspection plane and the
reference plane in XYZ space coordinates. In the XYZ color space,
each histogram distribution has a globe-like shape. Two histogram
distributions may be three-dimensionally overlapped or may be
three-dimensionally separated from each other. The respective
central coordinates are brought closer to each other by a shift
process. The inspection area K in the three-dimensional space is
divided into grids. Three-dimensional chromaticity histogram
distributions of T.sub.1(X,Y,Z) and T.sub.2(X, Y, Z) and minimum
distribution are determined, and the index is computed similarly.
An integrated number of grids may be projected on a plane, and an
integrated number in an overlap area in the plane on the grids by
similar integration. The XYZ chromaticity does not include
brightness information, so that the histogram distribution does not
change with a change in brightness of the image in the XYZ
space.
[0114] Instead of the XYZ color space histogram, an Lab color space
histogram may be used for evaluation of texture with reference to
the flowchart of FIG. 12. The above description of FIG. 11 is
generally applicable to the description of FIG. 12. In FIG. 12, the
process calculates average Lab values of the inspection area of the
image A and average Lab values of the inspection area of the image
B at S205. The Lab chromaticity includes brightness information, so
that the histogram distribution changes with a change in brightness
of the image in the Lab space.
[0115] The following describes a texture evaluation apparatus 201
of a metal surface 205 according to Embodiment 3 with reference to
FIG. 14. Like elements are expressed by like numerals in 200s and
are not specifically described. The following mainly describes the
differences.
[0116] As shown in FIG. 14, a color determination object is a
partial area of the metal surface 205, and a two-dimensional
colorimeter 202 is used to image an object area of the metal
surface 205. An arithmetic processor 203 includes an operator 203A
configured to calculate Lab values from stimulus values XYZ1 as a
reference, an operator 203B configured to calculate Lab values from
stimulus values XYZ2 as a determination object, an operator 203C
connected with the operator 203A and the operator 203B and
configured to calculate Lab average values, and a texture spread
index operator 203D configured to compute a texture spread index
from the reference Lab values and the object Lab values. The
arithmetic processor 203 sends the computed values from the
operators 203C and the 203D to a coloring device 257. The coloring
device 257 determines whether the metallic texture is appropriate,
based on the index value, with checking the image and additionally
performs a coloring process. A switch 206 is used to selectively
input either the reference XYZ values and the object XYZ values.
The primary processing flow is approximately similar to the
flowcharts of Embodiments 1 and 2 and is not specifically
described.
[0117] In computation of a chromaticity histogram distribution in
the Lab space corresponding to the inspection area K, the XYZ
values are converted into Lab values. The index is computed, based
on distributions in a three-dimensional space of L, a and b axes.
The Lab chromaticity distribution has a three-dimensional
elliptical shape. As shown in FIGS. 15A and 15B, U.sub.1(L,a,b) and
U.sub.2(L,a,b) respectively denote Lab values of the inspection
plane and the reference plane in Lab space coordinates. In the Lab
color space, each histogram distribution has a globe-like shape.
Two histogram distributions may be three-dimensionally overlapped
or may be three-dimensionally separated from each other. The
inspection area K in the three-dimensional space is divided into
grids. Three-dimensional chromaticity histogram distributions of
U.sub.1(L,a,b) and U.sub.2(L,a,b) and minimum distribution are
determined, and the index is computed similarly. An integrated
number of grids may be projected on a plane, and an integrated
number in an overlap area in the plane on the grids by similar
integration. The Lab chromaticity includes brightness information,
so that a change in brightness of the image leads to a change in L
value in the Lab space. The distributions U.sub.1 and U.sub.2 of
the degree of consistency are shifted in position in the Lab space.
This allows for determination taking into account the brightness.
This is because the different brightness of an image leads to the
different position of the distribution. For example, the Lab color
space histogram distribution is shifted downward with a decrease in
brightness and is shifted upward with an increase in
brightness.
[0118] The following describes other examples of applications. Two
taken images, i.e., images A and B, of a reference plane and an
inspection plane may be overlapped with each other, and their
chromaticity histogram distributions may be displayed on the
display unit 7. These chromaticity histogram distributions may be
shown in an overlapping manner on one chromaticity diagram. The
color difference may be determined by using average Lab values,
while the texture spread index indicating the texture of the metal
surface may be separately computed in percentage. This enables a
deviation of the spatial spread of the chromaticity distribution of
the inspection plane relative to the chromaticity distribution of
the reference plane or more specifically the irregularity and the
roughness to be checked numerically. The result of inspection is
shown numerically with respect to each area K. The width of the
grid is adjustable. The threshold value of the index may be set
arbitrarily. The measurement results and the taken images may be
stored. The aspects of the disclosure reduce the potential problem
of individual difference which is inevitable in visual inspection
and the potential trouble due to the difference from the clients'
criterion of judgment and allows for standardization of the metal
texture finishing and stable texture management.
[0119] The embodiments described above have the following
advantageous effects. The embodiments show examples of (1) the
average L value, the average a value and the average b value and
(2) the texture spread index of the two distributions H.sub.1(x,y)
and H.sub.2(x,y), the two distributions T.sub.1(X,Y,Z) and
T.sub.2(X, Y, Z) and the two distributions U.sub.1(L,a,b) and
U.sub.2(L,a,b). The difference in texture such as gloss, glaze,
irregularity and the like is provided separately from the color.
This ensures accurate and quick evaluation. The appropriate
direction is given for the finishing quality by adjustment of the
texture, such as adjustment of the roughness of the metal
surface.
[0120] As shown in Examples 1 to 7, the roughness of the metal
surface was measured for evaluation with the evaluation apparatus 1
of the metal surface according to the present disclosure.
[0121] The following describes the present disclosure more
concretely with reference to some examples, although the present
disclosure is not at all limited to these examples. The
characteristic values of the respective examples were measured and
evaluated as described below.
[0122] (1) Evaluation Apparatus [0123] An evaluation apparatus
PPLB-200 manufactured by PaPaLab Co., Ltd. was used. The apparatus
PPLB-200 includes a two-dimensional colorimeter RC-500. A light D50
manufactured by Panasonic Corporation was used for lighting.
[0124] (2) Imaging [0125] Imaging with the apparatus PPLB-200 was
performed in a dark room. A still image-type two-dimensional
colorimeter was used, and the measurement was performed on the
assumption that the L value of a white plate was 100.
[0126] (3) Measurement Range [0127] The measurement range for
evaluation had an identical size in all the samples. The
measurement ranges are shown by the square frame borders of
respective images in FIG. 16 with respect to Example 1, in FIG. 17
with respect to Example 2 and in FIGS. 18 and 19 with respect to
Example 3.
[0128] (4) Measurement Items and Results [0129] An acceptable
reference plane of a sample and inspection samples were measured.
The degree of consistency, .DELTA.E and differences of average Lab
values were determined with respect to the taken reference and
inspection sample images. With regard to the degree of consistency,
the xy-3D value is a value determined without the shift process of
the central coordinate of the histogram distribution (S147 in FIG.
6, S207 in FIG. 11 or S207 in FIG. 12), and the color separation
value is a value determined with the shift process of the central
coordinate of the histogram distribution. The color separation
value is a parameter of separating the texture from the color. The
differences of average Lab values are calculated by (average Lab
values of inspection sample)-(average Lab values of reference
sample). A sample having the most similar color and surface texture
to those of the reference sample was identified, based on the
measurement results of the degree of consistency and .DELTA.E. The
value .DELTA.E used was .DELTA.E00 taking into account the human
visibility characteristic.
[0130] The following describes measurement locations, degrees of
consistency, .DELTA.E00, differences of average Lab values in a
measurement range with respect to metal parts of Examples 1 to
3.
[0131] A non-brown turbidity portion of a metal surface of a sample
No. 2 was specified as a reference, and the degree of consistency,
.DELTA.E00, and differences of average Lab values were measured
with respect a brown turbidity portion and a non-brown turbidity
portion (location different from the reference) of the sample No.
2. Table 2 shows a list of measurement results. Evaluation of the
difference in texture with exclusion of the difference in color is
based on color separation values. Evaluation of the difference in
texture with the difference in color takes account of .DELTA.E00
and the differences of average Lab values, in addition to the color
separation values.
TABLE-US-00003 TABLE 2 Sample No. 2 Degree of consistency
Measurement Color Difference of average Lab portion xy-3D
separation .DELTA.E00 .DELTA.L .DELTA.a .DELTA.b Brown 46% 63%
3.530 -3.866 0.717 2.454 turbidity Non-brown 96% 96% 0.376 -0.124
0.253 -0.083 turbidity
[0132] Lab values in a reference measurement range, Lab values in a
measurement range of a brown turbidity portion and Lab values in a
measurement range of a non-brown turbidity portion with respect to
the sample No. 2 are respectively shown in Table 3, Table 4 and
Table 5. With respect to the degree of consistency of the non-brown
turbidity portion, both the xy-3D value and the color separation
value are 96%. With respect to the degree of consistency of the
brown turbidity portion, however, the color separation value is
63%, which is significantly higher than the xy-3D value of 46% by
17%. These results indicate the more accurate evaluation. This is
accordingly closer to the human visual recognition of brown
turbidity and thereby allows for quick and accurate evaluation.
TABLE-US-00004 TABLE 3 Sample No. 2 Reference Lab value in
measurement range L a b 73.424 1.622 6.321
TABLE-US-00005 TABLE 4 Sample No. 2 Brown turbidity Lab value in
measurement range L a b 71.733 1.848 7.424
TABLE-US-00006 TABLE 5 Sample No. 2 Non-brown turbidity Lab value
in measurement range L a b 73.674 1.505 6.385
Example 2
[0133] A non-brown turbidity portion of a metal surface of a sample
No. 3 was specified as a reference, and the degree of consistency,
.DELTA.E00, and differences of average Lab values were measured
with respect a brown turbidity portion and a non-brown turbidity
portion (location different from the reference) of the sample No.
3. Table 6 shows a list of measurement results. Evaluation of the
difference in texture with exclusion of the difference in color is
based on color separation values. Evaluation of the difference in
texture with the difference in color takes account of .DELTA.E00
and the differences of average Lab values, in addition to the color
separation values.
TABLE-US-00007 TABLE 6 Sample No. 3 Degree of consistency
Measurement Color Difference of average Lab portion xy-3D
separation .DELTA.E00 .DELTA.L .DELTA.a .DELTA.b Brown 66% 80%
5.217 -6.696 0.433 0.691 turbidity Non-brown 96% 96% 0.345 -0.350
-0.168 -0.078 turbidity
[0134] Lab values in a reference measurement range, Lab values in a
measurement range of a brown turbidity portion and Lab values in a
measurement range of a non-brown turbidity portion with respect to
the sample No. 3 are respectively shown in Table 7, Table 8 and
Table 9. With respect to the degree of consistency of the non-brown
turbidity portion, both the xy-3D value and the color separation
value are 96%. With respect to the degree of consistency of the
brown turbidity portion, however, the color separation value is
80%, which is significantly higher than the xy-3D value of 66% by
14%. These results indicate the more accurate evaluation. This is
accordingly closer to the human visual recognition of brown
turbidity and thereby allows for quick and accurate evaluation.
TABLE-US-00008 TABLE 7 Sample No. 3 Reference Lab value in
measurement range L a b 73.553 2.105 6.281
TABLE-US-00009 TABLE 8 Sample No. 3 Brown turbidity Lab value in
measurement range L a b 66.857 2.537 6.972
TABLE-US-00010 TABLE 9 Sample No. 3 Non-brown turbidity Lab value
in measurement range L a b 73.203 1.937 6.203
Example 3
[0135] Samples No. 4 to No. 6 had white turbidity, and a sample No.
7 did not have white turbidity. A sample No. 8 without white
turbidity was used as a reference. The degree of consistency,
.DELTA.E00, and differences of average Lab values were measured
with respect to white turbidity portions of the samples No. 4 to
No. 6. The sample No. 7 without white turbidity was similarly
measured for the purpose of comparison. Table 10 shows a list of
measurement results. Evaluation of the difference in texture with
exclusion of the difference in color is based on color separation
values. Evaluation of the difference in texture with the difference
in color takes account of .DELTA.E00 and the differences of average
Lab values, in addition to the color separation values.
TABLE-US-00011 TABLE 10 Measurement result of sample with white
turbidity by using sample No. 8 as reference Degree of consistency
Sample Color Difference of average Lab No. xy-3D separation
.DELTA.E00 .DELTA.L .DELTA.a .DELTA.b No. 4 59% 61% 14.190 16.946
3.703 -1.987 No. 5 54% 56% 19.464 22.925 4.258 -2.867 No. 6 67% 67%
11.437 13.739 3.221 -1.592 No. 7 85% 79% 2.575 3.301 0.692
-0.461
[0136] Lab values in a reference measurement range, Lab values in a
measurement range of a white turbidity portion and Lab values in a
measurement range of a non-white turbidity portion with respect to
the samples No. 8 (reference), No. 4, No. 5, No. 6 and No. 7 are
respectively shown in Table 11, Table 12, Table 13, Table 14 and
Table 15. With respect to the degree of consistency in the
non-white turbidity portion of the sample, the color separation
value is 79%, which is lower than the xy-3D value of 85%. With
respect to the degree of consistency in the samples No. 4 to No. 6
with white turbidity, however, the color separation values are
respectively 61%, 56% and 67%, which are all higher than the
corresponding xy-3D values by 2%. These results indicate the more
accurate evaluation. This is accordingly closer to the human visual
recognition of white turbidity and thereby allows for quick and
accurate evaluation.
TABLE-US-00012 TABLE 11 No. 8 (Reference) Lab value in measurement
range L a b 21.812 -1.804 -5.339
TABLE-US-00013 TABLE 12 No. 4 (Sample with white turbidity) Lab
value in measurement range L a b 38.758 1.899 -7.326
TABLE-US-00014 TABLE 13 No. 5 (Sample with white turbidity) Lab
value in measurement range L a b 44.737 2.454 -8.206
TABLE-US-00015 TABLE 14 No. 6 (Sample with white turbidity) Lab
value in measurement range L a b 35.551 1.417 -6.931
TABLE-US-00016 TABLE 15 No. 7 (Sample with white turbidity) Lab
value in measurement range L a b 25.113 -1.112 -5.800
[0137] The present disclosure is not limited to the above
embodiments, but various modifications may be made to the
embodiments without departing from the scope of the disclosure.
Such modifications as well as their equivalents are also included
in the scope of the disclosure. The disclosure may be implemented
by various aspects within the scope of the disclosure. The methods
of obtaining image information according to three spectral
sensitivities (S1(.lamda.), S2(.lamda.), S3(.lamda.)) described in
the above embodiments are only illustrative and are not
restrictive. The technical feature of the disclosure may also be
achieved by any other suitable method.
INDUSTRIAL APPLICABILITY
[0138] The evaluation apparatus of the present disclosure is
configured to quantify the texture such as gloss, glaze,
irregularity and the like of the metal surface by diffraction
phenomenon of illumination light. This quantification is
significantly close to the human visual recognition. The evaluation
apparatus of the disclosure is thus applicable to evaluation of the
irregularity of the metal surface or the like, which conventionally
depends on the human visual recognition.
REFERENCE SIGNS LIST
[0139] 1, 101, 201 . . . texture evaluation apparatus of metal
surface [0140] 2, 102, 202 . . . two-dimensional colorimeter [0141]
3, 103, 203 . . . arithmetic processor [0142] 5, 105, 205 . . .
metal surface [0143] 6, 106, 206 . . . lighting unit [0144] 7 . . .
display device [0145] 21 . . . photographic lens [0146] 22a, 22b,
22c . . . optical filters [0147] 23 . . . imaging element [0148]
22a', 22c' . . . dichroic mirrors [0149] 23a, 23b, 23c . . .
imaging elements
* * * * *
References