JP2020153759A - Evaluation device, genuine feeling evaluation method, and program - Google Patents

Abstract

To clarify the feature constituting qualitative genuine feeling and quantitatively evaluate the qualitative genuine feeling using a physical feature quantity so as to match with visual observation.SOLUTION: Provided is an evaluation device for evaluating the genuine feeling of an object on the basis of an image in which the surface of the object is imaged. This evaluation device comprises: statistical quantity calculation means for calculating a statistical quantity based on a chromaticity distribution of the object in the imaged image; divided integral value acquisition means for integrating spatial frequency characteristics based on the change amount of surface properties of the object in the imaged image separately in at least two or more frequency bands according to the surface properties of the object and obtaining the respective integral value; and evaluation value calculation means for calculating, using subjective evaluation points with respect to genuine feeling, an evaluation value that indicates the genuine feeling of the object on the basis of the statistical quantity and the integral value.SELECTED DRAWING: Figure 6

Description

The present invention relates to an evaluation device, an authenticity evaluation method and a program.

Conventionally, artificial or synthetic leather (hereinafter referred to as synthetic leather) that imitates the texture of natural leather has been used for inexpensive clothing, shoes, and the like. In addition, synthetic leather has come to be adopted in products in which natural leather has been conventionally used due to the recent rise in the price of natural leather. Therefore, it is required that the texture of synthetic leather is closer to that of natural leather.

Patent Document 1 discloses a technique for evaluating the texture of "irritability" caused by a resin mass on the surface of a suede-like synthetic leather based on an image feature amount, for the purpose of enhancing the texture of the synthetic leather.

However, although the texture of synthetic leather is judged only by the qualitative "likeness" that humans feel when they see natural leather, the characteristics that make up the natural leather-likeness that humans feel are still clear. is not. The qualitative "likeness" that humans feel when they see natural leather is called "genuine feeling".

The present invention has been made in view of the above, clarifies the features constituting the qualitative authenticity, and quantitatively adjusts the qualitative authenticity to the visual inspection using physical features. The purpose is to evaluate.

In order to solve the above-mentioned problems and achieve the object, the present invention is an evaluation device for evaluating the authenticity of the object based on an image of the surface of the object, in the captured image of the object. A statistic calculation means for calculating a statistic based on a chromaticity distribution and a spatial frequency characteristic based on a fluctuation amount of the surface texture in a captured image of the object are at least two or more depending on the surface texture of the object. An evaluation value indicating the real feeling of the object based on the statistic and the integrated value by using a divided integrated value acquisition means for dividing into frequency bands and obtaining each integrated value and a subjective evaluation point for the real feeling. It is characterized in that it is provided with an evaluation value calculation means for calculating.

According to the present invention, it is possible to quantitatively evaluate the qualitative real feeling from the measured physical features.

FIG. 1 is a schematic view showing a configuration example of an evaluation system according to an embodiment. FIG. 2 is a block diagram showing a hardware configuration of the control unit. FIG. 3 is a functional block diagram showing the functions of the control unit. FIG. 4 is a diagram showing an example of an apparatus for a subjective evaluation experiment. FIG. 5 is a diagram showing the results of the subjective evaluation test. FIG. 6 is a flowchart schematically showing the flow of the authenticity evaluation process by the control unit. FIG. 7 is a diagram showing the relationship between the authenticity and the absolute value term. FIG. 8 is a correlation diagram between the authenticity evaluation amount (evaluation value) and the subjective evaluation points performed in advance.

The evaluation device, the authenticity evaluation method, and the embodiment of the program will be described in detail with reference to the accompanying drawings.

FIG. 1 is a schematic view showing a configuration example of the evaluation system 100 according to the embodiment. As shown in FIG. 1, the evaluation system 100 includes a first image acquisition device 10, a second image acquisition device 20, and a control unit 5.

The first image acquisition device 10 acquires an image for obtaining a feature amount relating to the surface texture of the sample. The first image acquisition device 10 includes a light source 1 which is an example of a projection unit, a sample table 3, an angle changing unit 2 which changes the irradiation angle of the light source 1 with respect to the sample table 3, and a camera which is an example of an imaging unit. 4 and.

As the light source 1, a xenon light source having a spectral distribution close to that of sunlight is used. The light source 1 is moved by the angle changing unit 2 with respect to the normal L direction of the sample table 3 and is appropriately positioned in a desired angle direction.

A sample O for evaluating the surface texture is attached to the sample table 3. The camera 4 is arranged in the normal L direction of the sample table 3.

The camera 4 takes a picture of the sample O attached to the sample table 3 and acquires a two-dimensional image. In the present embodiment, a general digital camera, an RGB camera such as a CCD, or the like is used as the camera 4.

On the other hand, the second image acquisition device 20 acquires an image for obtaining a feature amount related to the chromaticity distribution of the sample. The second image acquisition device 20 includes a light source 11 which is an example of a projection unit, a sample table 13, an angle changing unit 12 which changes the irradiation angle of the light source 11 with respect to the sample table 13, and a camera which is an example of an imaging unit. It is equipped with 14.

As the light source 11, a xenon light source having a spectral distribution close to that of sunlight is used. The light source 11 is moved by the angle changing unit 2 with respect to the normal L direction of the sample table 3 and is appropriately positioned in a desired angle direction.

A sample O for evaluating the chromaticity distribution is attached to the sample table 13. The camera 14 is arranged in a direction inclined by 45 ° with respect to the normal L direction of the sample table 13.

The camera 14 photographs the sample O attached to the sample table 13 to acquire a two-dimensional image. In this embodiment, a hyperspectral camera (spectral camera) is used as the camera 14. The hyperspectral camera is a camera capable of measuring the brightness of each wavelength of light, and can obtain a spectral spectrum at each pixel. The spectroscopic camera of the present embodiment can acquire a wavelength range of 400 nm to 700 nm every 10 nm.

The camera 14 is not limited to the hyperspectral camera, and an RGB camera such as a digital camera or a CCD can also be used. However, since the information of only RGB 3ch is converted to L * a * b * instead of the spectrum, the color measurement accuracy is deteriorated.

Here, FIG. 2 is a block diagram showing a hardware configuration of the control unit 5. As shown in FIG. 2, the control unit 5 includes a CPU 5a (Central Processing Unit) 5a, a ROM (Read Only Memory) 5b, a RAM (Random Access Memory) 5c, an HDD 5d, and the like. The control unit 5 uses the RAM 5c as a work memory according to the authenticity evaluation program stored in advance in the ROM 5b and the HDD 5d, and uses the RAM 5c as a work memory to display each part of the first image acquisition device 10 such as the light source 1, the angle changing unit 2, and the camera 4. Drive control. Further, the control unit 5 uses the RAM 5c as a work memory according to the authenticity evaluation program stored in advance in the ROM 5b and the HDD 5d, and uses the light source 11, the angle changing unit 12, the camera 14, and the like to obtain the second image. Drive and control each part. As the control unit 5, for example, a personal computer (desktop, notebook computer) can be used.

The authenticity evaluation program executed by the control unit 5 of the present embodiment is a file in an installable format or an executable format such as a CD-ROM, a flexible disk (FD), a CD-R, or a DVD (Digital Versatile Disk). It may be configured to be recorded and provided on a computer-readable recording medium.

Further, the authenticity evaluation program executed by the control unit 5 of the present embodiment may be stored on a computer connected to a network such as the Internet and provided by downloading via the network. Further, the authenticity evaluation program executed by the control unit 5 of the present embodiment may be provided or distributed via a network such as the Internet.

The control unit 5 evaluates the surface texture and the chromaticity distribution of the sample O based on the two-dimensional image taken by the camera 4 and the camera 14. That is, the control unit 5 also functions as an evaluation device.

The function of the control unit 5 as an evaluation device will be described with reference to the functional block diagram of FIG. As shown in FIG. 3, the control unit 5 operates the CPU 5a according to the authenticity evaluation program, so that the statistic calculation means 51, the division integral value acquisition means 52, the evaluation value calculation means 53, and the chromaticity distribution acquisition means 54 , The surface texture acquisition means 55, the spatial frequency characteristic calculation means 56, and the storage unit 57.

The chromaticity distribution acquisition means 54 acquires the chromaticity distribution in the captured image of the sample O as an object. In addition, the surface texture acquisition means 55 acquires the surface texture in the captured image of the sample O as the object. The chromaticity distribution acquisition means 54 and the surface texture acquisition means 55 acquire captured images of the sample O as an object acquired from measurement angle conditions of at least two angles or more.

The statistic calculation means 51 calculates a statistic based on the chromaticity distribution in the captured image of the sample O as the object.

The spatial frequency characteristic calculating means 56 calculates the spatial frequency characteristic based on the amount of variation in the surface texture in the captured image of the sample O as the object, and weights the spatial frequency characteristic by the visual spatial frequency characteristic. The spatial frequency characteristic calculation means 56 changes the visual spatial frequency characteristic according to the observation distance of the sample O as an object, and weights the spatial frequency characteristic of the fluctuation amount of the surface texture.

The division integral value acquisition means 52 obtains spatial frequency characteristics based on the amount of variation in the surface texture of the sample O to be the object in the captured image, and at least two or more frequency bands according to the surface texture of the sample O to be the object. Integrate separately to obtain the integrated value of each.

The evaluation value calculation means 53 uses the subjective evaluation points for the real feeling to obtain the real feeling of the target sample O based on the statistic based on the chromaticity distribution in the captured image of the target sample O and the integrated value. Calculate the indicated evaluation value.

The sensory evaluation information acquired in advance is stored in the storage unit 57.

In order to evaluate the authenticity with the evaluation system 100 of the present invention, a scaled sensory evaluation point (subjective evaluation point) is required in addition to the physical quantity measurement of the sample O. Therefore, a subjective evaluation experiment of genuine feeling is performed in advance, and the sensory evaluation points (subjective evaluation points) obtained by the experiment are stored in the storage unit 57 as sensory evaluation information.

The subjective evaluation experiment will be described below. For the subjective evaluation experiment, a method called a paired comparison method was used. The paired comparison method randomly presents a pair of objects to be evaluated to the subject, and asks the subject to score whether the object on the right has a real feeling with respect to the object on the left, for example. Is.

In order to carry out a subjective evaluation experiment by the Scheffe method, which is a paired comparison method, a total of 10 types of samples of natural leather and synthetic leather were prepared and prepared. Table 1 below shows a list of samples used this time.

The colors of the samples used this time were all black, which is the most widely distributed, and the size of the samples was A4 size (length: about 300 mm, width: about 210 mm). FIG. 4 is a diagram showing an example of an apparatus for a subjective evaluation experiment. As shown in FIG. 4, since the texture of leather changes depending on the observation angle, the sample O was placed on the jig 50 in a curved shape. The observation distance was 300 mm. The subject compared the pair of left side sample A and right side sample B in the presented curved state, and based on the authenticity visually perceived from the surface of the right side sample B with respect to the left side sample A, as follows. Scores were given.

When B feels more authentic than A +3 points When B feels more authentic than A +2 points When B feels more authentic than A +1 point A = B 0 points When A feels more authentic than B -1 point When A feels more authentic than B -2 points A is much stronger and more authentic than B When you feel a feeling -3 points

In this experiment, the score was given in 7 steps, but other steps such as 9 steps and 11 steps may be used. As the number of steps increases, smaller differences can be detected, but the burden on the subject increases.

The subject evaluated from a position 300 mm away. In this experiment, since there are 10 kinds of observation conditions, a total of ₁₀ C ₂ = 45 pairs was evaluated. Experiments were conducted on a total of eight image evaluation engineers as subjects. The evaluation points for each sample object were calculated by applying the analysis method of quantification type III to the results of the above evaluation experiment.

The evaluation results are shown in FIG. The horizontal axis of FIG. 5 is a number (serial number) assigned to the sample object, and the sample objects are arranged in the order of subjective evaluation points. The vertical axis shows the subjective evaluation points of each sample object, and the smaller the value, the more artificially felt it is. That is, the evaluation result was that the sample object "1" was the most genuine and the sample object "4" was the most artificial.

Hereinafter, the operation of the authenticity evaluation method executed by the evaluation system 100 having the above configuration will be described.

First, the image acquired by the first image acquisition device 10 will be described.

The sample O to be evaluated is set on the sample table 3. The first image acquisition device 10 turns on the light source 1, irradiates the sample O attached to the sample table 3 with light, and measures with the camera 4 installed in the normal direction L of the sample table 3. Since the texture of leather changes depending on the observation angle, the light source 1 is moved by the angle changing unit 2 and illuminated with respect to the camera 4 at an angle of highlight and shade. Here, the highlight is a range tilted by 10 to 25 ° with respect to the camera 4 (normal direction L of the sample O), and the shade is 45 to 60 ° with respect to the camera 4 (normal direction L of the sample O). Refers to the tilted range. By setting the highlight angle condition close to the specular reflection condition and the shade angle condition far from the specular reflection condition as the measurement angle conditions, the evaluation is performed using the two-angle features in which the texture change due to the angle change is larger. Can be done. In this embodiment, the measured values at 15 ° and 60 ° are used (geometry: 15 ° / 0 °, 60 ° / 0 °). This makes it possible to consider changes in texture depending on the observation angle. The image acquired by the first image acquisition device 10 has a measurement size of 3000 × 3000pixel (imaging range: about 75 mm × 75 mm) and a resolution of about 1000 dpi.

Next, the image acquired by the second image acquisition device 20 will be described.

The sample O to be evaluated is set on the sample table 13. The second image acquisition device 20 is a camera 14 installed by turning on the light source 11, irradiating the sample O attached to the sample table 13 with light, and tilting 45 ° from the normal direction L of the sample table 13. Make a measurement. Since the color of leather changes depending on the observation angle, the light source 11 is moved by the angle changing unit 2, and the highlight angle and the shade angle are set in the normal direction L with respect to the camera 14 and the specular reflection angle and the specular reflection angle. It was tilted and illuminated. Here, the highlight refers to a range tilted by 20 to 35 ° with respect to the normal direction L of the sample O, and the shade refers to a range tilted by 0 to -15 ° with respect to the normal direction L of the sample O. By setting the highlight angle condition close to the specular reflection condition and the shade angle condition far from the specular reflection condition as the measurement angle conditions, the evaluation is performed using the two-angle features in which the texture change due to the angle change is larger. Can be done. In this embodiment, the highlight is 30 ° and the shade is -15 ° (geometry: 45 ° / 45 °, 30 ° / 45 °, -15 ° / 45 °). This makes it possible to consider changes in texture depending on the observation angle. The image acquired by the second image acquisition device 20 has a measurement size of 600 × 600 pixel (imaging range: about 15 mm × 15 mm) and a resolution of about 1000 dpi.

Subsequently, the flow of the authenticity evaluation process executed by the control unit 5 will be described.

Here, FIG. 6 is a flowchart schematically showing the flow of the authenticity evaluation process by the control unit 5. As shown in FIG. 6, the control unit 5 acquires a measurement image (captured image) by using the first image acquisition device 10 and the second image acquisition device 20 (step S1).

Next, the control unit 5 converts the measurement image (captured image) into an L * a * b * image in the L * a * b * color system, and acquires the chromaticity distribution (step S2). More specifically, the control unit 5 converts an RGB image or a spectroscopic image into an XYZ image using the following equations (1) and (2) in order to calculate L * a * b *.

Here, S (λ) is the spectral distribution of illumination, x (λ), y (λ), and z (λ) are color matching functions, R (λ) is the spectral reflectance, and k is a coefficient. k is calculated from the formula (3) shown below. As for the spectral reflectance, the imaging data of the sample was standardized and the reflectance was obtained by using the imaging data of the white reference plate having the reflectance of 100% and the reflection characteristics close to the perfect diffused radiation.

Then, the control unit 5 converts the XYZ image into an L * a * b * image using the equation (4) shown below.

Here, Xn, Yn, and Zn are tristimulus values on the perfect diffuse reflection surface defined by the International Commission on Illumination (CIE), and are Xn = 96.42, Yn = 100, and Zn = 82.49. In this embodiment, S (λ) is a spectral distribution of D65. By converting to a color matching system that suits human senses in this way, the correlation with visual evaluation can be improved.

Returning to FIG. 6, the control unit 5 (statistical calculation means 51) uses the L * image of the L * a * b * images obtained by the second image acquisition device 20 to determine the kurtosis and skewness. Calculate (step S3). Kurtosis and skewness are a type of statistic and are indicators of how much the population deviates from the normal distribution.

Kurtosis is a statistic that indicates how sharp the distribution is from the normal distribution, and indicates the degree of kurtosis of the mountain and the degree of spread of the hem. A positive value is obtained when the distribution is sharper than the normal distribution, and a negative value is obtained when the distribution is flatter than the normal distribution (0 in the case of the normal distribution).

On the other hand, skewness is a statistic that shows how much the distribution is distorted from the normal distribution, and is an index that shows left-right symmetry. A positive value is taken when the right tail is long or biased to the left, and a negative value is taken when the left tail is long or biased to the right (0 in the case of a symmetrical distribution).

Next, the control unit 5 (spatial frequency characteristic calculation means 56) calculates the spatial frequency characteristic using the L * image among the L * a * b * images obtained by the first image acquisition device 10 (spatial frequency characteristic calculation means 56). Step S4). More specifically, the control unit 5 (spatial frequency characteristic calculation means 56) subtracts the L * image average value from the value at each pixel of the L * image to acquire the L * deviation image.

The deviation ΔL * of L * is uniformly low in a sample such as a metal that reflects as a whole because the average value rises, and the small part that has little influence on the average value is locally particularly strong. In the state of reflection, so-called glittering appearance, a large value is taken in the portion of the pixel that appears to shine. That is, the pixels whose reflection is locally strengthened are perceived by the human eye as grain or unevenness. The control unit 5 (spatial frequency characteristic calculation means 56) Fourier transforms the acquired L * deviation image with respect to the deviation Δ in all the pixels. After the Fourier transform, the control unit 5 (spatial frequency characteristic calculation means 56) averages the amplitudes at each frequency and calculates the one-dimensional spatial frequency characteristics.

Next, the control unit 5 (spatial frequency characteristic calculation means 56) weights the spatial frequency characteristic obtained in step S4 with the “visual spatial frequency characteristic (VTF) with respect to the observation distance” (step S5). In this embodiment, the function shown in the following equation (5) is used as the spatial frequency characteristic of vision. As the spatial frequency characteristic of vision, a function related to other visual characteristics already known may be used.

Here, the unit of υ is cycle / degree. Since the cycle / degree can be obtained by the equation (5), the grain calculated in consideration of the visual characteristics is taken into consideration by multiplying the equation (5) calculated according to the observation distance of the sample by the one-dimensional spatial frequency characteristic. And unevenness can be evaluated. Thereby, the spatial frequency characteristic can be calculated according to the resolution of the human eye according to the assumed observation distance.

Next, the control unit 5 (division integral value acquisition means 52) divides the weighted spatial frequency characteristic acquired in step S5 into several frequency bands according to the surface texture of the leather (step S6). In this embodiment, the leather sample used is divided into three frequency bands. The first is 0 to 0.1 cycle / mm, which corresponds to unevenness of leather. The second is 0.1 to 1 cycle / mm, which corresponds to a coarse grain. Finally, the third is 1 to 4 cycles / mm, which corresponds to fine grain. By doing so, the range of the frequency band to be divided can be defined to some extent, and the integrated value corresponding to each surface texture feature can be calculated.

Next, the control unit 5 (divided integration value acquisition means 52) integrates the frequency bands divided in step S6 as integration value acquisition means (step S7).

Finally, the control unit 5 (evaluation value calculating means 53) evaluates the authenticity by the following equation (6) using the subjective evaluation points and the feature amounts calculated in each of steps S2, S3, and S6. The value is calculated (step S8). Regression analysis using the stepwise method was used to calculate the evaluation formula. However, in the stepwise method, since the model with the best coefficient of determination is output, the meaning of the evaluation formula, that is, the logic of selecting the explanatory variables is not considered. Therefore, we adjusted the output of the stepwise method so that it would be a meaningful evaluation formula while maintaining statistical significance.

As shown in Table 2 below, x1, x2, x3, x4, x5, and x6 are explanatory variables, respectively, and are 45 ° / 45 ° L * and 60 ° / 0 ° spatial frequency characteristics (0.1 to 1 cycle /). mm), -15 ° / 45 ° sharpness, 15 ° / 0 ° spatial frequency characteristic (0 to 0.1 cycle / mm), 60 ° / 0 ° spatial frequency characteristic (0 to 0.1 cycle / mm), 60 ° / 0 ° spatial frequency characteristic (1 to 4 cycles / mm).

Further, as shown in Table 3 below, p1, p2, p3, p4, p5, p6, p7, p8 and p9 are coefficients. The coefficient is a parameter determined based on each subjective evaluation point. Therefore, when the evaluation stage of the subjective evaluation experiment is changed from, for example, 7 stages to 9 stages and the subjective evaluation score is calculated, the parameters change. In this experiment, as shown in Table 3 below, p1 = 0.3, p2 = -0.02, p3 = 5.37, p4 = -1.06, p5 = -0.06, p6 = 0. 69, p7 = 13.21, p8 = −0.5, p9 = −0.5 (p <0.05).

なお、式（６）のパラメータは、説明変数を標準化して決定してもよい。標準化を行うことにより、単位や平均値などが異なるデータ同士を単純に比較できるようになる。

The parameters of equation (6) may be determined by standardizing the explanatory variables. By standardizing, it becomes possible to simply compare data with different units and average values.

From equation (6), the real feeling is the frequency band of 0.1 to 1 cycle / mm when photographed under shade conditions, that is, the feature amount corresponding to the rough grain on the sample surface and the brightness value under specular reflection conditions. The larger the absolute value of the added value and the frequency band of 0 to 0.1 cycle / mm when shooting under the highlight condition and shade condition, that is, the larger the feature amount corresponding to the unevenness on the sample surface, the higher the value. You can see that it gets bigger.

On the other hand, it can be seen that the value becomes smaller when the frequency band of 1 to 4 cycles / mm when photographed under the shade condition, that is, the feature amount corresponding to the fine grain on the sample surface is large. The reason why the term of brightness under the rough grain of the shade and the specular reflection condition is an absolute value is that the subjective evaluation result shows that the larger the positive and negative values of the absolute value term, the larger the score of the real feeling. Is.

FIG. 7 shows the relationship between the authenticity and the absolute value term. The horizontal axis of FIG. 7 is a number (serial number) assigned to the sample object, and the sample objects are arranged in the order of subjective evaluation points. The vertical axis shows the subjective evaluation points (real feeling) and absolute value terms of each sample object. The larger the positive and negative values of the absolute value argument, the larger the score of authenticity.

FIG. 8 shows a correlation diagram between the authenticity evaluation amount (evaluation value) calculated by the authenticity evaluation method as described above and the subjective evaluation points performed in advance. The contribution rate R ² is 0.92, and it can be seen that the evaluation amount obtained in this embodiment is extremely close to the human subjectivity. From the above, the present invention has made it possible to develop a method for evaluating the feeling of authenticity, which has a high correlation with visual inspection.

As described above, according to the present embodiment, it is possible to quantitatively evaluate the qualitative real feeling (natural leather-likeness) from the measured physical features. In short, unlike the conventional evaluation method, the evaluation formula for the authenticity is constructed from the physical features obtained by multi-angle condition measurement for the leather sample for the subjective evaluation points for the authenticity acquired in advance. It is possible to quantitatively evaluate the authenticity that has been qualitative until now.

Although the embodiments of the present invention have been described in detail with reference to the drawings, each of the above embodiments is merely an example of the present invention, and the present invention is not limited to the configuration of each of the above embodiments. .. Even if there is a design change or the like within a range that does not deviate from the gist of the present invention, it is included in the present invention.

5 Evaluation device 51 Statistics calculation means 52 Divided integral value acquisition means 53 Evaluation value calculation means 54 Saturation distribution acquisition means 55 Surface texture acquisition means 56 Spatial frequency characteristic calculation means

Japanese Unexamined Patent Publication No. 2017-015496

Claims (8)

In an evaluation device that evaluates the authenticity of an object based on an image of the surface of the object.
A statistic calculation means for calculating a statistic based on the chromaticity distribution in the captured image of the object, and
The spatial frequency characteristic based on the fluctuation amount of the surface texture in the captured image of the object is divided into at least two or more frequency bands according to the surface texture of the object and integrated to obtain the integrated value of each. Value acquisition method and
An evaluation value calculation means for calculating an evaluation value indicating the authenticity of the object based on the statistic and the integral value using the subjective evaluation points for the real feeling.
An evaluation device characterized by comprising. A chromaticity distribution acquisition means for acquiring the chromaticity distribution in the captured image of the object, and
A surface texture acquisition means for acquiring the surface texture in the captured image of the object, and
With
The chromaticity distribution acquisition means and the surface texture acquisition means acquire captured images of the object acquired from measurement angle conditions of at least two angles or more.
The evaluation device according to claim 1, wherein the evaluation device is characterized by the above. The measurement angle condition of two or more angles includes a highlight angle condition close to the specular reflection condition and a shade angle condition far from the specular reflection condition.
The evaluation device according to claim 2, wherein the evaluation device is characterized by the above. A spatial frequency characteristic calculation means for calculating the spatial frequency characteristic based on the fluctuation amount of the surface texture and weighting with the visual spatial frequency characteristic is provided.
The spatial frequency characteristic calculating means changes the visual spatial frequency characteristic according to the observation distance of the object, and weights the spatial frequency characteristic of the fluctuation amount of the surface texture.
The evaluation device according to any one of claims 1 to 3, wherein the evaluation device is characterized by the above. The divided integral value acquisition means includes a frequency band having a wavelength longer than 0.1 cycle / mm and a frequency band having a wavelength shorter than 0.1 cycle / mm as the frequency band divided into at least two or more.
The evaluation device according to any one of claims 1 to 4, wherein the evaluation device is characterized by the above. For the chromaticity distribution in the captured image of the object and the surface texture in the captured image of the object, the L *, a *, b * values in the L * a * b * color system are used.
The evaluation device according to any one of claims 1 to 5, wherein the evaluation device is characterized by the above. This is a method for evaluating the authenticity of an object in an evaluation device that evaluates the authenticity of the object based on an image of the surface of the object.
A statistic calculation step for calculating a statistic based on the chromaticity distribution in the captured image of the object, and
The spatial frequency characteristic based on the fluctuation amount of the surface texture in the captured image of the object is divided into at least two or more frequency bands according to the surface texture of the object and integrated to obtain the integrated value of each. Value acquisition process and
An evaluation value calculation step of calculating an evaluation value indicating the authenticity of the object based on the statistic and the integral value using the subjective evaluation points for the authenticity.
Authenticity evaluation method characterized by including. A computer that controls an evaluation device that evaluates the authenticity of the object based on an image of the surface of the object.
A statistic calculation means for calculating a statistic based on the chromaticity distribution in the captured image of the object, and
The spatial frequency characteristic based on the fluctuation amount of the surface texture in the captured image of the object is divided into at least two or more frequency bands according to the surface texture of the object and integrated to obtain the integrated value of each. Value acquisition method and
An evaluation value calculation means for calculating an evaluation value indicating the authenticity of the object based on the statistic and the integral value using the subjective evaluation points for the real feeling.
A program to function as.

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