JP2023133002A - Gray hair evaluation system and gray hair evaluation method - Google Patents

Abstract

To provide a gray hair evaluation system and a gray hair evaluation method capable of accurately evaluating gray hair by remote control.SOLUTION: The gray hair evaluation system comprises: acquisition means which acquires an image including hair; spectrum acquisition means which acquires a spectrum image based on the image acquired by the acquisition means; and evaluation means which evaluates gray hair included in the hair based on the spectrum image acquired by the spectrum acquisition means.SELECTED DRAWING: Figure 1

Description

The present invention relates to a gray hair evaluation system and a gray hair evaluation method.

As a method for evaluating gray hair, for example, there is a method for evaluating gray hair disclosed in Patent Document 1.

In Patent Document 1, the disorder of the hair group is eliminated by specifying a measurement site to be inspected and arranging the hair group present in the specified measurement site via a hair fixing member to obtain an arrayed hair group. Disclosed is a method for evaluating gray hair that makes it possible to evaluate gray hair in a state where the hairs are aligned.

Japanese Patent Application Publication No. 2005-296352

However, in the gray hair evaluation method disclosed in Patent Document 1, a hair group is aligned through a hair fixing member to be inspected, so a member for fixing specific hair is required. Therefore, since it takes time to evaluate gray hair and also requires a technique for fixing the hair, there is a problem that gray hair cannot be evaluated by remote control.

Therefore, the present invention was developed in order to solve the above-mentioned problems, and its purpose is to provide a gray hair evaluation system and method that can evaluate gray hair with high precision and by remote control. It's about doing.

A gray hair evaluation system according to a first aspect of the present invention includes an acquisition means for acquiring an image including hair, a spectrum acquisition means for acquiring a spectrum image based on the image acquired by the acquisition means, and a spectrum image acquired by the spectrum acquisition means. and evaluation means for evaluating gray hair contained in the hair based on the above.

In the gray hair evaluation system according to a second invention, in the first invention, the evaluation means sets a specific wavelength included in a predetermined wavelength range as a normalized wavelength based on the spectrum image acquired by the spectrum acquisition means, and A specific wavelength included in the wavelength range is selected as the evaluation wavelength, and between the normalized wavelength and the evaluation wavelength, gray hairs included in the hair are determined based on the difference in spectral intensity between the normalized wavelength and the evaluation wavelength. It is characterized by evaluating.

In the gray hair evaluation system according to a third aspect, in the second aspect, the evaluation means has a specific wavelength included in a frequency band of 730 to 830 nm as a first normalized wavelength, and a specific wavelength included in a frequency band of 430 to 530 nm. is selected as the first evaluation wavelength.

In the gray hair evaluation system according to a fourth aspect of the invention, in the first aspect, the evaluation means uses, as learning data, a dataset consisting of a reference spectral image acquired in advance and an evaluation of gray hair, and inputs the reference spectral image. The method is characterized in that the output is an evaluation of the gray hair, and the gray hair contained in the hair is evaluated based on the spectrum image acquired by the spectrum acquisition means using an evaluation model generated by machine learning.

In the gray hair evaluation system according to a fifth invention, in any one of the first to fourth inventions, the spectrum acquisition means determines a reference wavelength indicating a hair wavelength acquired in advance from the image acquired by the acquisition means. The method is characterized in that a wavelength for which a difference between the two wavelengths is equal to or less than a threshold value is extracted, and the spectrum image indicating the extracted wavelength is acquired.

A gray hair evaluation system according to a sixth invention is characterized in that in any one of the first to fourth inventions, the spectrum acquisition means uses a dataset consisting of a reference image acquired in advance and a spectrum image showing hair as learning data. a spectral image showing the hair based on the image acquired by the acquisition means, using an extraction model generated by machine learning, with the input as a reference image and the output as a spectral image. Features.

In the gray hair evaluation system according to a seventh aspect of the present invention, in any one of the first to fifth aspects, the acquisition means acquires a whiteboard image obtained by capturing a whiteboard, and the spectrum acquisition means acquires a whiteboard image obtained by the acquisition means. The method is characterized in that a spectrum image is acquired based on the relative reflectance of the image with respect to the white board image.

In the gray hair evaluation system according to an eighth aspect, in the seventh aspect, the acquisition means acquires imaging condition information indicating the imaging conditions when the white board image is imaged, and according to the acquired imaging condition information, the gray hair evaluation system It is characterized by correcting.

In the gray hair evaluation system according to a ninth invention, in any one of the first to eighth inventions, the acquisition means sharpens the image based on the smoothed image and the unsmoothed image. It is characterized by acquiring.

A gray hair evaluation method according to a tenth invention includes an acquisition step of acquiring an image including hair, a spectrum acquisition step of acquiring a spectrum image based on the image acquired by the acquisition step, and a spectrum acquired by the spectrum acquisition step. The present invention is characterized by causing a computer to execute an evaluation step of evaluating gray hair contained in the hair based on the image.

According to the first to tenth inventions, the gray hair evaluation system and the gray hair evaluation method evaluate gray hair contained in hair based on a spectral image. This makes it possible to evaluate gray hair with high precision and remote control.

In particular, according to the second invention, gray hair contained in hair is evaluated based on the difference in spectral intensity between the normalized wavelength and the evaluation wavelength. This makes it possible to separate the features of the captured spectral image. Therefore, gray hair can be evaluated with higher accuracy.

In particular, according to the third invention, a specific wavelength included in the frequency band of 730 to 830 nm is selected as the first normalized wavelength, and a specific wavelength included in the frequency band of 430 to 530 nm is selected as the first evaluation wavelength. Thereby, wavelengths in appropriate frequency bands can be used as the first normalized wavelength and the first evaluation wavelength, so that more accurate evaluation can be performed.

In particular, according to the fourth invention, gray hair contained in hair is evaluated using an evaluation model based on a spectral image. This makes it possible to evaluate gray hair with high precision using machine learning.

In particular, according to the fifth invention, a wavelength whose difference from a reference wavelength indicating a hair wavelength obtained in advance is equal to or less than a threshold is extracted from the image, and a spectral image indicating the extracted wavelength is obtained. This makes it possible to extract only the hair included in the image and exclude noise from the scalp, etc., making it possible to evaluate gray hair with higher accuracy.

In particular, according to the sixth invention, a spectral image showing hair is acquired based on the image using the extraction model. This makes it possible to extract only the hair included in the image and exclude noise from the scalp, etc., making it possible to evaluate gray hair with higher accuracy.

According to the seventh invention, a spectrum image is acquired based on the relative reflectance of the image with respect to the white board image. As a result, a spectral image is acquired using the white board image as a reference, so it is possible to use an image from which noise has been eliminated.

According to the eighth invention, imaging condition information is acquired, and the image is corrected according to the acquired imaging condition information. This makes it possible to correct conditions such as the amount of light at the time of photographing, and it becomes possible to use an image free of noise.

According to the ninth invention, a sharpened image is obtained based on the smoothed image and the image before smoothing. This makes it possible to sharpen an image, for example, based on the difference between a smoothed image and an image before smoothing. This makes it possible to use an image sharpened with accuracy.

FIG. 1 is a schematic diagram showing an example of the configuration of a gray hair evaluation system according to the present embodiment. FIG. 2(a) is a schematic diagram showing an example of the configuration of the gray hair evaluation device according to the present embodiment, and FIG. 2(b) is a schematic diagram showing an example of the function of the gray hair evaluation device according to the present embodiment. FIG. 3 is a diagram showing an example of a flowchart of the operation of the gray hair evaluation system in this embodiment. FIG. 4(a) is a diagram showing an example of an image. FIG. 4(b) is a diagram showing an example of a spectrum image. FIG. 4(c) is a diagram showing an example of a gray hair spectrum. FIG. 5 is a diagram showing an example of an extraction model in this embodiment. FIG. 6 is a graph showing an example of spectral intensity for each wavelength. FIG. 7 is a diagram showing an example of an evaluation model in this embodiment.

An example of a gray hair evaluation system according to an embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is a schematic diagram showing an example of the configuration of a gray hair evaluation system 100. In the gray hair evaluation system 100, for example, as shown in FIG. 1, a gray hair evaluation device 1, a server 3, and a user terminal 2 having an imaging device 5 and a lighting device 6 are connected via a public communication network 4. Further, the gray hair evaluation system 100 may include only the gray hair evaluation device 1 and the imaging device 5, which are connected without going through the public communication network 4.

The server 3 is a storage medium that stores various data such as images transmitted from the gray hair evaluation device 1 and the user terminal 2. Further, the server 3 transmits various stored data to the gray hair evaluation device 1 and the user terminal 2 as necessary. For example, the server 3 may have at least some of the functions of the gray hair evaluation device 1, and may perform at least some of the processing instead of the gray hair evaluation device 1, for example.

The public communication network 4 is, for example, the Internet network to which the gray hair evaluation device 1 is connected via a communication circuit. The public communication network 4 may be constituted by a so-called optical fiber communication network. Further, the public communication network 4 may be realized by a known communication technology such as a wireless communication network in addition to a wired communication network. The user terminal 2 is owned by, for example, a user of a service using the gray hair evaluation system 100, and is connected to the gray hair evaluation device 1 via the public communication network 4. The user terminal 2 may represent, for example, an electronic device that generates a database. As the user terminal 2, for example, an electronic device such as a personal computer or a tablet terminal is used. The user terminal 2 may have at least some of the functions of the gray hair evaluation device 1, for example. The user terminal 2 may include a display (not shown) or a speaker that can present the gray hair evaluation results to the user.

The lighting fixture 6 is any lighting fixture that illuminates the user's head when the imaging device 5 captures an image including the user's hair.

The imaging device 5 is a known camera that images the user's hair and generates an image and a spectrum image. As the imaging device 5, for example, an RGB camera, a multispectral camera, a target spectrum camera, a hyperspectral camera, etc. may be used. The imaging device 5 may be, for example, a video camera that shoots moving images, or may be built into the gray hair evaluation device 1. When the imaging device 5 is a spectral video camera, for example, a spectral image may be extracted from a part of the captured moving image. The imaging device 5 outputs the captured image to the user terminal 2. Further, the imaging device 5 may output the captured image to the gray hair evaluation device 1 via the public communication network 4 without going through the user terminal 2.

The gray hair evaluation device 1 performs processing based on the image and spectrum image output from the user terminal 2, respectively. The gray hair evaluation device 1 may be an electronic device such as a personal computer (PC), a smartphone, a tablet terminal, a wearable terminal, an IoT (Internet of Things) device, Raspberry Pi (registered trademark), etc. A single board computer may be used, and for example, the imaging device 5 may be built-in.

The gray hair evaluation device 1 acquires, for example, an image including hair output from the imaging device 5, acquires a spectral image based on the acquired image, and evaluates gray hair contained in the hair based on the acquired spectral image.

Next, with reference to FIG. 2, an example of the gray hair evaluation device 1 in this embodiment will be described. FIG. 2(a) is a schematic diagram showing an example of the configuration of the gray hair evaluation device 1 according to the present embodiment, and FIG. 2(b) is a schematic diagram showing an example of the function of the gray hair evaluation device 1 according to the present embodiment. be.

As shown in FIG. 2A, for example, the gray hair evaluation device 1 includes a housing 10, a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, and a storage device. 104 and I/Fs 105 to 107. The CPU 101, ROM 102, RAM 103, storage unit 104, and I/Fs 105 to 107 are connected by an internal bus 110.

The CPU 101 controls the gray hair evaluation device 1 as a whole. The ROM 102 stores operation codes for the CPU 101. The RAM 103 is a work area used when the CPU 101 operates. The storage unit 104 stores various information such as images, spectrum images, extraction models, and evaluation models. As the storage unit 104, for example, a data storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), an SD card, or a miniSD card is used. Note that, for example, the gray hair evaluation device 1 may include a GPU (Graphics Processing Unit), which is not shown.

The I/F 105 is an interface for transmitting and receiving various information via the public communication network 4. I/F 106 is an interface for transmitting and receiving information to and from input unit 108 . For example, a keyboard is used as the input unit 108 , and an evaluator or the like using the gray hair evaluation device 1 inputs various information or control commands for the gray hair evaluation device 1 via the input unit 108 . I/F 107 is an interface for transmitting and receiving various information to and from display unit 109 . The display unit 109 outputs various information such as the evaluation results stored in the storage unit 104 or the processing status of the gray hair evaluation device 1. A display is used as the display unit 109, and may be of a touch panel type, for example.

The storage unit 104 stores, for example, images and spectral images acquired from the imaging device 2, as well as information related to the spectral images, algorithms used for evaluating gray hair, extraction models, evaluation models, and the like.

The display unit 109 displays various information. The display unit 109 displays, for example, evaluation results, images, and the like.

FIG. 2(b) is a schematic diagram showing an example of the functions of the gray hair evaluation device 1. The gray hair evaluation device 1 includes an acquisition section 11 , an image processing section 12 , an evaluation section 13 , a storage section 14 , an output section 15 , a conversion section 16 , and an extraction section 17 . Note that the acquisition unit 11, image processing unit 12, evaluation unit 13, storage unit 14, output unit 15, conversion unit 16, and extraction unit 17 shown in FIG. This is realized by using the RAM 103 as a work area and executing a program stored in the storage unit 104 or the like, and may be controlled by artificial intelligence, for example.

The acquisition unit 11 acquires images and spectral images. In addition to acquiring the image captured by the imaging device 5 via the user terminal 2, the acquisition unit 11 may also acquire an image and a spectrum image from the imaging device 5 built into the gray hair evaluation device 1, for example. . Alternatively, images and spectrum images may be acquired from the server 3 or the like via the public communication network 4. Note that the frequency and cycle at which the acquisition unit 11 acquires various information are arbitrary.

The image processing unit 12 performs processing such as correction on the image acquired by the acquisition unit 11.

The converter 16 acquires a spectral image based on the image processed by the image processor 12. In such a case, for example, the converting unit 16 converts the image into a spectral image indicating a spectrum included in the image.

The extraction unit 17 extracts a spectral image representing hair from the spectral image acquired by the conversion unit 16.

The evaluation unit 13 evaluates gray hair based on the spectrum image extracted by the extraction unit 17.

The storage unit 14 retrieves various information stored in the storage unit 104 as necessary. The storage unit 14 stores various information acquired or output by the acquisition unit 11 , the image processing unit 12 , the evaluation unit 13 , the conversion unit 16 , and the extraction unit 17 in the storage unit 104 .

The output unit 15 outputs various information. The output unit 15 transmits the evaluation results to the display unit 109 via the I/F 107.

Next, an example of the operation of the gray hair evaluation system 100 in this embodiment will be described. FIG. 4 is a flowchart showing an example of the operation of the gray hair evaluation system 100 in this embodiment.

First, in step S110, the acquisition unit 11 acquires an image including hair. In such a case, for example, an image captured by the imaging device 2 may be acquired via the public communication network 4. Further, when capturing an image with the imaging device 2, the lighting device 6 may be used to irradiate light onto the user's head, but this is not limited to this, and the image may be captured without using the lighting device 6. good. Further, for example, an image containing hair stored in the server 3 may be acquired via the public communication network 4. The acquisition unit 11 may acquire an image captured by the imaging device 5 via the I/F 105. The acquisition unit 11 stores the image in the storage unit 104 via the storage unit 14, for example. The image including hair is, for example, an image including human hair as shown in FIG. 4(a), and may also include the human scalp.

Next, in step S120, the image processing unit 12 performs processing such as correction on the image acquired by the acquisition unit 11 in step S110. The image processing unit 12 may, for example, smooth the image and sharpen the image based on the smoothed image and the image before smoothing. In such a case, the image processing unit 12 first smoothes the image using, for example, a median filter, a Gaussian filter, a moving average, the Savitzky-Golay method, or the like. Next, the image processing unit 12 sharpens the image using a Laplacian filter (second order differential) and an unsharp method. In such a case, the image processing unit 12 calculates, for example, the difference between the smoothed image and the image before smoothing, and sharpens the image so as to increase the difference. Smoothing is a process of blurring an image, for example, reducing changes in pixel values of the image. Sharpening is, for example, a process of increasing changes in pixel values of an image.

Next, in step S130, the conversion unit 16 acquires a spectrum image based on the image processed by the image processing unit 12. The conversion unit 16 acquires a spectrum image based on the relative reflectance of the image with respect to a whiteboard image obtained by capturing a whiteboard, for example. The white board is a reference board that requires a certain reflectance. This makes it possible to calculate and compare the relative reflectance of gray hair and other constituents, regardless of variations in the illumination light source.

Further, in step S130, the conversion unit 16 may correct the image based on the imaging conditions that are the conditions when imaging the white board and the imaging conditions when imaging human hair. . The imaging conditions include, for example, gain, exposure time, brightness, etc. when imaging. For example, the conversion unit 16 corrects the gain and exposure time of the image based on the difference between the light intensity of the lighting fixture 6 when capturing the white board image and the light intensity of the lighting fixture 6 when capturing the image.

Next, in step S140, the extraction unit 17 extracts a spectrum representing hair from the spectrum image acquired in step S130. For example, the extraction unit 17 extracts from the image a wavelength whose difference from a reference wavelength indicating a hair wavelength acquired in advance is equal to or less than a threshold value, and acquires a spectrum image indicating the extracted wavelength. For example, the extraction unit 17 uses a SAM (Spectral Angle Mapper) method or the like to extract a spectrum that approximates a reference wavelength that indicates the hair wavelength that has been obtained in advance. The reference wavelength is, for example, any wavelength selected from within the wavelength range of 380 nm to 900 nm. This makes it possible to extract only the hair included in the image and eliminate noise such as the scalp.

In addition, in step S140, the extraction unit 17 uses a data set consisting of a reference image and a spectral image showing hair acquired in advance as learning data, and generates the image by machine learning with the input as the image and the output as the spectral image. Using the extracted extraction model, a spectral image showing hair may be extracted based on the image acquired in step S110.

As a method for generating the extraction model, for example, machine learning using a neural network as a model may be used to generate the extraction model. The extracted model may be generated using machine learning using a neural network such as CNN (Convolution Neural Network) as a model, or any other model may be used.

In such a case, the extraction model stores a relationship having a degree of association between a reference image that is input data and a spectral image showing hair that is output data, as shown in FIG. 5, for example. The degree of association indicates the degree of connection between input data and output data, and for example, it can be determined that the higher the degree of association, the stronger the connection between each piece of data. The degree of association may be expressed in three or more values or three or more levels, such as a percentage, or may be expressed in binary values or two levels. Further, the reference image is an image including hair to be used as learning data acquired in advance. Further, the spectral image showing the hair may be a spectral image obtained by the same method as in step S140. The spectral image showing hair may be, for example, a spectral image obtained by extracting a spectrum similar to a reference wavelength representing the wavelength of hair acquired in advance from a spectral image including hair using the SAM method or the like.

For example, the association is constructed based on the degree of connection between a plurality of input data, a pair, and a plurality of output data. The association is appropriately updated in the process of machine learning, and indicates a classifier that uses a function that is optimized based on, for example, a plurality of input data and a plurality of output data. Note that the association may include, for example, a plurality of degrees of association indicating the degree of connection between each piece of data. For example, when a database is constructed using a neural network, the degree of association can be made to correspond to a weight variable. For example, as shown in FIG. 5, the association may indicate the degree of connection between a plurality of input data and a plurality of output data. In this case, by using the association, the relationship between each input data of "Image A" to "Image C" in FIG. It is possible to associate and memorize the degree. Therefore, a plurality of input data can be linked to one output data, for example, through association. Thereby, it is possible to realize multifaceted selection of output data with respect to input data.

The association includes, for example, a plurality of degrees of association that link each input data and each output data, respectively. The degree of association is expressed in three or more levels, such as a percentage, 10 levels, or 5 levels, and is expressed, for example, as line characteristics (such as thickness). For example, "Image A" included in the input data shows a degree of association AA "73%" with "Spectral image A" included in the output data, and a correlation degree of AA "73%" with "Spectral image B" included in the output data. The degree of association AB is "12%". That is, the "degree of association" indicates the degree of connection between each piece of data, and for example, the higher the degree of association, the stronger the connection between each piece of data.

The degree of association of three or more levels shown in FIG. 5 is obtained in advance. In other words, in determining the actual solution, whether input data or output data was adopted and evaluated can be determined by accumulating past datasets and analyzing them to determine the relationship shown in Figure 5. Build up your degree.

For example, assume that "spectral image B" was determined to have the highest suitability for input data "image B" and was evaluated in the past. By collecting and analyzing such data sets, the degree of correlation between input data and output data becomes stronger.

This analysis may be performed by artificial intelligence. In such a case, for example, if there are many cases where "spectral image B" is estimated for the input data "image B", the degree of association between "image B" and "spectral image B" is calculated. Set higher.

Further, this degree of association may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficient for the output of this neural network node corresponds to the degree of association described above. Moreover, it is not limited to a neural network, and may be composed of any decision-making factors that constitute artificial intelligence.

Furthermore, the extraction model may include at least one hidden layer between input data and output data, and may be subjected to machine learning. The above-described degree of association is set for either or both of the input data and the hidden layer data, and this serves as a weighting for each data, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

This degree of association is what artificial intelligence calls trained data. After creating such trained data, a new spectral image representing hair is actually estimated from the image. In such a case, a spectral image showing hair for the image processed in step S120 is newly acquired. At the time of estimation, for example, the degree of association shown in FIG. 5 obtained in advance is referred to. For example, if a newly acquired image is the same as or similar to "Image A", the degree of association with "Spectrum Image A" is "73%", "Spectral Image A" is The degree of association with image B is 12%. In this case, "spectral image A" with the highest degree of association is selected as the optimal solution. However, it is not essential to select the one with the highest degree of association as the optimal solution, and it is also possible to select "spectral image B", which has a low degree of association but is recognized as being related, as the optimum solution. In addition, it is of course possible to select an output solution with no connected arrows, and any other priority order may be used as long as it is based on the degree of association.

By referring to such degree of association, it is possible to quantitatively select output data suitable for the input data, not only when the images are the same or similar to the input data, but also when they are dissimilar. .

Next, in step S150, the evaluation unit 13 evaluates gray hair contained in the hair based on the spectrum image showing the hair extracted in step S140. For example, the evaluation unit 13 selects a specific wavelength included in a predetermined wavelength range as a normalized wavelength, a specific wavelength included in a predetermined wavelength range as an evaluation wavelength, and evaluates the normalized wavelength based on a spectrum image showing hair. Gray hair contained in the hair is evaluated based on the difference in spectral intensity between the normalized wavelength and the evaluation wavelength. The normalized wavelength is a wavelength to be normalized in order to reduce the influence of different conditions such as light unevenness and shadows, and to evaluate gray hair. The evaluation wavelength is a wavelength for evaluation to normalize the normalized wavelength.

FIG. 6 is a plurality of spectral graphs shown by the spectral image. FIG. 6 is a graph in which the vertical axis represents the intensity of light and the horizontal axis represents the wavelength [nm]. Each of the solid lines and broken lines of the plurality of spectral graphs corresponds to, for example, a spectrum of each captured image. For example, the evaluation unit 13 selects a specific wavelength included in the frequency band of 730 to 830 nm as the first normalized wavelength, and selects a specific wavelength included in the frequency band of 430 to 530 nm as the first evaluation wavelength. The evaluation unit 13 may use, for example, 780 nm as the first normalized wavelength and 480 nm as the first evaluation wavelength.

The evaluation unit 13 selects a wavelength included in such a spectrum graph as a specific wavelength, and as a normalized wavelength and an evaluation wavelength. The evaluation unit 13 may set the wavelength at which the difference value of the spectral intensity between the respective spectral graphs is the largest as the specific wavelength. Furthermore, the evaluation unit 13 may specify a singular point where a convex peak is formed on each spectrum graph as a specific wavelength. Furthermore, the evaluation unit 13 may select the specific wavelength by analysis using a trained model using regression analysis, brute force analysis, machine learning, or the like.

Here, for example, the normalized wavelength and the evaluation wavelength may be specified at one point, or may be specified at a plurality of points. Furthermore, a wavelength range centered around these normalized wavelengths and evaluation wavelengths may be set. The wavelength range may be a predetermined wavelength range set in advance, such as a wavelength width where the difference between the normalized wavelength and the evaluation wavelength is ±10 nm. Therefore, if the normalized wavelength is 780 nm and the wavelength range is ±10 nm, the range in which spectrum data is actually evaluated will be 770 to 790 nm. In such a case, the normalization wavelength and the evaluation wavelength may be determined by, for example, setting the wavelength at the center of each wavelength range as the specific wavelength.

Next, the evaluation unit 13 calculates a gray hair level indicating the amount of gray hair between the selected normalized wavelength and the evaluation wavelength based on the difference in spectral intensity between the normalized wavelength and the evaluation wavelength, and evaluates gray hair. .

The evaluation unit 13 calculates the gray hair level based on a spectral change due to a difference in spectral intensity between the normalized wavelength and the evaluation wavelength, for example. In such a case, by normalizing with the sum of the spectral intensities of the normalized wavelength and the evaluation wavelength, even if the conditions are different, such as uneven light or shadows, the effects of these can be reduced and the level of gray hair can be reduced. can be made comparable. These gray hair levels are calculated using the following equation using, for example, a known spectral measurement method or spectral analysis technique (for example, "NDSI: normalized difference spectral index, normalized spectral reflectance index"). “I _λ ” is, for example, the spectral intensity of “λ _nm ”, the normalized wavelength is determined as “λ2”, and the evaluation wavelength is determined as “λ1”.

The evaluation unit 13 assigns a gray hair level based on the pixel distribution for each region, for example, based on the evaluation result of the spectral image. Further, as the gray hair level, a specific degree of gray hair such as level 1 to level 5 may be indicated according to the amount of gray hair per area, for example.

The evaluation unit 13 generates an evaluation result using format data such as an output format stored in the storage unit 104, for example. The evaluation unit 13 stores the evaluation results in the storage unit 104 via the storage unit 14, for example.

In addition, in step S150, the evaluation unit 13 uses a dataset consisting of a reference spectral image acquired in advance and an evaluation of gray hair as learning data, uses the input as a reference spectral image, and outputs as an evaluation of gray hair. Using the evaluation model generated through learning, gray hair contained in hair may be evaluated based on the spectral image. The reference spectral image is a spectral image used as learning data for the evaluation model, and is, for example, a previously acquired spectral image showing hair, but is not limited to this, and any image including a spectrum showing hair may be used. good. The evaluation of gray hair is, for example, a spectrum image showing a gray hair spectrum, which is a spectrum representing gray hair. Furthermore, the evaluation of gray hair may be, for example, the level of gray hair. Furthermore, the evaluation of gray hair may be a result of evaluation of gray hair obtained by a method similar to step S150 or a spectrum image showing gray hair. In other words, the evaluation of gray hair is based on a spectrum image, selects a specific wavelength included in a predetermined wavelength range as the normalized wavelength, selects a specific wavelength included in the predetermined wavelength range as the evaluation wavelength, and then compares the normalized wavelength and the evaluated wavelength. In between, the gray hair level may be calculated based on the difference in spectral intensity between the normalized wavelength and the evaluation wavelength, and the spectral image may be an acquired spectral image showing gray hair based on the calculated gray hair level.

As a method for generating an evaluation model, for example, machine learning using a neural network as a model may be used to generate the evaluation model. The evaluation model may be generated using machine learning using a neural network model such as CNN (Convolution Neural Network), or any other model may be used.

In such a case, the evaluation model stores a relationship having a degree of association between the reference spectral image that is the input data and the gray hair evaluation that is the output data, as shown in FIG. 7, for example.

Furthermore, the evaluation model may include at least one hidden layer between input data and output data, and may be subjected to machine learning. The above-described degree of association is set for either or both of the input data and the hidden layer data, and this serves as a weighting for each data, and the output is selected based on this. Then, when this degree of association exceeds a certain threshold value, the output may be selected.

After creating such learned data, the evaluation of gray hair will actually be estimated from a new spectral image. In such a case, a new evaluation of gray hair is obtained for the spectrum image extracted in step S140.

By referring to this degree of association, it is possible to quantitatively select output data suitable for the input data, whether the spectral image is the same or similar to the input data, or even when it is dissimilar. can.

Next, the output unit 15 outputs the evaluation results. The output unit 15 outputs the evaluation results to the display unit 109 or the like. Further, the output unit 15 may output the evaluation result to the user terminal 2 via the public communication network 4.

Thereby, the operation of the gray hair evaluation device 1 in this embodiment ends. This makes it possible to evaluate gray hair with high precision and remote control.

Although an embodiment of the invention has been described, this embodiment is presented by way of example and is not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included within the scope and gist of the invention, as well as within the scope of the invention described in the claims and its equivalents.

1 : Gray hair evaluation device 2 : User terminal 3 : Server 4 : Public communication network 5 : Imaging device 6 : Lighting fixture 10 : Housing 11 : Acquisition part 12 : Image processing part 13 : Evaluation part 14 : Storage part 15 : Output part 16: Conversion unit 17: Extraction unit 100: Gray hair evaluation system 101: CPU
102:ROM
103: RAM
104: Storage section 105: I/F
106: I/F
107: I/F
108: Input section 109: Display section 110: Internal bus

Claims (10)

an acquisition means for acquiring an image including hair; a spectrum acquisition means for acquiring a spectral image based on the image acquired by the acquisition means;
A gray hair evaluation system comprising: evaluation means for evaluating gray hair contained in the hair based on the spectrum image acquired by the spectrum acquisition means. The evaluation means selects a specific wavelength included in a predetermined wavelength range as a normalized wavelength, a specific wavelength included in a predetermined wavelength range as an evaluation wavelength, based on the spectrum image acquired by the spectrum acquisition device, and Gray hair according to claim 1, characterized in that gray hair contained in the hair is evaluated based on a difference in spectral intensity between the normalized wavelength and the evaluation wavelength. Rating system. A claim characterized in that the evaluation means selects a specific wavelength included in a frequency band of 730 to 830 nm as a first normalized wavelength, and selects a specific wavelength included in a frequency band of 430 to 530 nm as a first evaluation wavelength. The gray hair evaluation system described in 2. The evaluation means uses, as learning data, a dataset consisting of a reference spectral image obtained in advance and an evaluation of gray hair, and uses the input as the reference spectral image and the output as the evaluation of gray hair, which is generated by machine learning. The gray hair evaluation system according to claim 1, characterized in that gray hair contained in the hair is evaluated based on the spectrum image acquired by the spectrum acquisition means using an evaluation model. The spectrum acquisition means extracts, from the image acquired by the acquisition means, a wavelength whose difference from a reference wavelength indicating a wavelength of hair acquired in advance is equal to or less than a threshold value, and generates the spectrum image indicating the extracted wavelength. The gray hair evaluation system according to any one of claims 1 to 4, characterized in that: The spectrum acquisition means uses a data set consisting of a reference image acquired in advance and a spectrum image showing hair as learning data, and uses the input as the reference image and the output as the spectrum image to extract data generated by machine learning. The gray hair evaluation system according to any one of claims 1 to 4, wherein a spectral image showing the hair is acquired based on the image acquired by the acquisition means using a model. The acquisition means further acquires a whiteboard image obtained by capturing the whiteboard,
The spectrum acquisition means acquires a spectrum image based on the relative reflectance of the image with respect to the white board image acquired by the acquisition means.
The gray hair evaluation system according to any one of claims 1 to 5, characterized by: The gray hair according to claim 7, wherein the acquisition means acquires imaging condition information indicating the imaging conditions when the white board image was imaged, and corrects the image according to the acquired imaging condition information. Rating system. The gray hair evaluation according to any one of claims 1 to 8, wherein the acquisition means acquires the sharpened image based on the smoothed image and the image before smoothing. system. an acquisition step of acquiring an image including hair; a spectral acquisition step of acquiring a spectral image based on the image acquired by the acquisition step;
A method for evaluating gray hair, comprising: causing a computer to execute an evaluation step of evaluating gray hair contained in the hair based on the spectrum image acquired in the spectrum acquisition step.

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