Patent ID: 12196545

EXEMPLARY EMBODIMENTS

Next, exemplary embodiments of the present invention will be described with reference to the drawings.

First Exemplary Embodiment

FIG.1is a block diagram of a surface property estimation system100according to a first exemplary embodiment of the present invention. The surface property estimation system100is an information processing system configured to estimate the surface property of an object from an image in which a surface of the object such as an industrial product is captured and extract the feature amount unique to the object, and record them in association with each other.

The surface property estimation system100includes, as main components, a camera110, a measuring sensor120, a communication interface unit (hereinafter referred to as a communication I/F unit)130, an operation input unit140, a screen display unit150, a storage unit160, and an arithmetic processing unit170.

The camera110is an imaging means for capturing an image of a surface of an object. The camera110may be a visible-light color camera or a black-and-white camera equipped with a charge-coupled device (CCD) image sensor or a complementary MOS (CMOS) image sensor having a pixel capacity of about several millions pixels, for example.

The measuring sensor120is a sensor for measuring the surface property of an object. As the measuring sensor120, a measuring sensor of any type such as a contact scanning method, an optical interference method, an image synthesis method by means of focus shift, a confocal method (laser measurement), or the like may be used.

The communication I/F unit130is configured of a dedicated data communication circuit, and is configured to perform data communication with various types of devices connected over a wireless network or the like. The operation input unit140is configured of operation input devices such as a keyboard and a mouse, and is configured to detect operation by an operator and output it to the arithmetic processing unit170. The screen display unit150is configured of a screen display device such as a liquid crystal display (LCD) or a plasma display panel (PDP), and is configured to display, on a screen, various types of information such as a matching position according to an instruction from the arithmetic processing unit170.

The storage unit160is configured of storage devices such as a hard disk and a memory, and is configured to store therein processing information and a program1601necessary for various types of processing in the arithmetic processing unit170. The program1601is a program for implementing various processing units by being read and executed by the arithmetic processing unit170, and is read in advance from an external device (not illustrated) or a storage medium (not illustrated) via a data input-output function such as the communication I/F unit130and is stored in the storage unit160. The main processing information stored in the storage unit160includes training data1602, an estimation model1603, an image1604, a surface property1605, an individual identifier1606, and a database1607.

The training data1602includes a plurality of pairs each consisting of a surface image of an object captured by the camera110and the surface property represented by the image. The estimation model1603is a model learned by means of an object recognition method through deep learning using the training data1602. When an image is input, the estimation model1603outputs the surface property estimated from the image.

The image1604is an image of a surface of an object captured by the camera110. The surface property1605is a surface property estimated from the image1604by the estimation model1603. the individual identifier1606is a feature amount extracted from the image1604. The database1607is configured to store therein an individual identifier of an object and a surface property in association with each other.

The arithmetic processing unit170has a microprocessor such as an MPU and the peripheral circuits, and is configured to read and execute the program1601from the storage unit160to allow the hardware and the program1601to cooperate with each other to thereby implement the various processing units. The main processing units implemented by the arithmetic processing unit170includes a training data creation unit1701, a learning unit1702, an image acquisition unit1703, an estimation unit1704, an extraction unit1705, a registration unit1706, and a matching unit1707.

The training data creation unit1701is configured to image a predetermined area of an object surface by the camera110. The training data creation unit1701is also configured to measure the surface property of a predetermined area of an object surface by the measuring sensor120. The training data creation unit1701is also configured to create the training data1602from an image of a predetermined area of an object surface captured by the camera110and the surface property of the predetermined area of the object surface measured by the measuring sensor120, and store it in the storage unit160.

The learning unit1702is configured to read the training data1602from the storage unit160, and by using the read training data1602, create by the machine learning the estimation model1603in which an image of an object surface is an input and the surface property represented by the image is an output, and store it in the storage unit160. The learning unit1702is configured to construct the estimation model1603on a convolutional neural network (CNN) for example.

The image acquisition unit1703is configured to image a predetermined area of an object surface by the camera110, and store the captured image1604in the storage unit160.

The estimation unit1704is configured to read the estimation model1603and the image1604from the storage unit160, input the image1604to the estimation model1603, and store the surface property1605output from the estimation model1603into the storage unit160.

The extraction unit1705is configured to read the image1604from the storage unit160, extract the feature amount unique to the image from the image1604, and store the extracted feature amount in the storage unit160as the individual identifier1606. As a method of extracting the feature amount unique of the image from the image1604, a publicly-known method may be used. For example, the extraction unit1705may determine at least one location where a luminance change is steep and the position can be obtained stably as a feature point from the image1604, and put the local luminance pattern around the feature point into data as a feature amount.

The registration unit1706is configured to read the surface property1605and the individual identifier1606from the storage unit160, and store them in association with each other in the database1607.

The matching unit1707is configured to read the individual identifier1606from the storage unit160, and perform matching between the individual identifier1606and an individual identifier stored in the database1607. Matching with an individual identifier may be performed by using any publicly known method. For example, when the individual identifier is one in which the local luminance pattern around the feature point is put into data as a feature amount, the matching unit1707may perform matching by a method as described below. First, the matching unit1707obtains, from the two individual identifiers, feature points in which the difference in the feature amount becomes minimum as a pair. Then, the matching unit1707extracts, from obtained groups of pairs, only pairs whose relative positional relation with another feature point does not contradict. Then, assuming that the total number of feature points obtained from the two individual identifiers is represented by N, the number of feature point pairs in which the geometric arrangement is correct is represented by “n”, the matching unit1707calculates a ratio “s” of “n” to N as a matching score of the two individual identifiers. Finally, when the matching score “s” is higher than a predetermined threshold, the matching unit1707determines that the two individual identifiers are identical, while when it is not higher, the matching unit1707determines that the two individual identifiers are different.

The matching unit1707is configured to display the matching result on the screen display unit150, and/or transmit it to an external device via the communication I/F unit130. For example, the matching unit1707is configured to read the surface property stored in association with the matched individual identifier from the database1607, display the readout surface property on the screen display150, and/or transmit it to an external device via the communication I/F unit130.

Next, operation of the surface property estimation system100will be described. Operation of the surface property estimation system100is largely divided into a learning operation, a registration operation, and a matching operation. In the learning operation, the training data1602is created, and the estimation model1603is learned with use of the created training data1602. In the registration operation, an image of an object to be registered is captured, and from the captured image, the surface property is estimated with use of the estimation model1603, and also an individual identifier is extracted from the captured image, and the estimated surface property and the extracted individual identifier are registered in the database1607in association with each other. In the matching operation, an image of an object that is a matching target is captured, an individual identifier is extracted from the captured image, and matching is performed between the extracted individual identifier and an individual identifier registered in the data, and the matching result is output. Hereafter, details of these operations will be described.

<Learning Operation>

FIG.2is a flowchart illustrating an example of a learning operation. With reference toFIG.2, the training data creation unit1701repeats steps S2to S4the defined number of times (steps S1, S5). The defined number of times is any number of times if it is larger than the number of times in which a required amount of training data is created. For example, when there are a plurality of objects for learning, the surface property estimation system100repeats steps S2to S4one or more times for each object for learning. At step S2, the training data creation unit1701images a predetermined area on a surface of an object for learning by the camera110. At step S3, the training data creation unit1701measures the surface property of the predetermined area by the measuring sensor120. At step S4, the training data creation unit1701creates one unit of training data by associating the image acquired at step S2and the surface property acquired at step S3, and stores it in the storage unit160.

FIG.3is a schematic diagram illustrating an operation of the training data creation unit1701. InFIG.3, a reference numeral181denotes an object for learning, and a reference numeral182denotes an inspection area set on a surface of the object181. The shape, size and arranged location on the object surface of the inspection area182are fixed in advance. Hereinafter, it is assumed that the surface of the object181is almost flat and the inspection area182is rectangular. Further, as a coordinate system, a rectangular coordinate system is set in which an X axis is set in parallel with a long axis direction, a Y axis is set in parallel with a short axis direction, and a Z axis is set perpendicular to the XY plane, with a lower left end point of the inspection area182being the starting point. However, the shape of the inspection area182is not limited to rectangular, and any shape is acceptable. Also, the coordinate system is not limited to the rectangular coordinate system as described above, and may be a polar coordinate system.

Further, inFIG.3, a reference numeral183denotes a grayscale image of the inspection area182captured by the camera110. The image183is configured of n by m pieces of pixels Gij (i=1, 2, . . . , m, j=1, 2, . . . , n). Further, inFIG.3, a reference numeral184denotes three-dimensional point group data of the inspection area182measured by the measuring sensor120. The three-dimensional point group data184is configured of n by m pieces of point data Dij (i=1, 2, . . . , m, j=1, 2, . . . , n). Each piece of the point data Dij represents a three-dimensional location (x, y, z) in the rectangular coordinate system of a point on the object surface. Each piece of point data Dij corresponds to a pixel Gij of the image183one to one. That is, the point data Dij and the pixel Gij have the same X coordinate value and the same Y coordinate value. On the other hand, the Z coordinate value of the point data Dij represents the height of the point on the inspection area182specified by the X coordinate value and the Y coordinate value.

In the example illustrated inFIG.3, as the surface property, three-dimensional point group data that is a group of points corresponding to pixels of the image183is used. However, the surface property is not limited to the three-dimensional point group data. The surface property may be surface roughness or the like that can be statistically derived from the three-dimensional point group data. Examples of surface roughness include surface roughness parameters such as arithmetic mean estimation (Ra) and maximum height (Rz). Alternatively, the surface property may be a normal vector of the surface shape corresponding to the pixels of the image183.

Referring toFIG.2again, the learning unit1702uses the training data1602created by the training data creation unit1701and creates by the machine learning the estimation model1603in which an image of an object surface is an input and the surface property represented by the image is an output, and stores it in the storage unit160(step S6).

<Registration Operation>

FIG.4is a flowchart illustrating an example of a registration operation. Referring toFIG.4, the image acquisition unit1703images an inspection area of a surface of an object to be registered by the camera110, and stores the obtained image1604in the storage unit160(step S11). Then, the estimation unit1704inputs the image1604acquired by the image acquisition unit1703in the learned estimation model1603, and stores the surface property1605output from the estimation model1603in the storage unit160(step S12). Next, the extraction unit1705extracts the feature amount unique to the image from the image1604acquired by the image acquisition unit1703, and stores the extracted feature amount into the storage unit160as the individual identifier1606(step S13). Then, the registration unit1706stores the surface property1605estimated by the estimation unit1704and the individual identifier1606extracted by the extraction unit1705in the database1607in association with each other (step S14). When there are a plurality of objects to be registered, the surface property estimation system100repeats the same operation as that described above the number of times that is the same as the number of objects to be registered.

FIG.5is a schematic view illustrating the registration operation. InFIG.5, a reference numeral185denotes an object to be registered, and a reference numeral186denotes an inspection area set on a surface of the object185. The shape, size and arranged location on the object surface of the inspection area186are the same as those at the time of learning. Further, inFIG.5, a reference numeral187denotes a grayscale image of the inspection area186captured by the camera110. The image187is configured of n by m pieces of pixels Gij (i=1, 2, . . . , m, j=1, 2, . . . , n) that are the same as those at the time of learning. Further, inFIG.5, a reference numeral188denotes the surface property of the inspection area186estimated from the image186using the estimation model1603. The surface property188is configured of n by m pieces of point data Dij (i=1, 2, . . . , m, j=1, 2, . . . , n) that are the same as those at the time of learning. Further, inFIG.5, a reference numeral189denotes an individual identifier extracted from the image187by the extraction unit1705. The individual identifier189and the surface property188are registered in the database1607in association with each other as illustrated inFIG.5.

<Matching Operation>

FIG.6is a flowchart illustrating an example of a matching operation. Referring toFIG.6, the image acquisition unit1703images an inspection area on a surface of an object to be registered by the camera110, and stores the obtained image1604in the storage unit160(step S21). Then, the extraction unit1705extracts the feature amount unique to the image from the image1604acquired by the image acquisition unit1703, and stores the extracted feature amount in the storage unit160as the individual identifier1606(step S22). Then, the matching unit1707performs matching between the individual identifier1606extracted by the extraction unit1705and an individual identifier stored in the database1607(step S23). Then, the matching unit1707displays the matching result on the screen display unit150, and/or transmits it to an external device via the communication I/F unit130(step S24).

FIG.7is a schematic diagram illustrating the matching operation. InFIG.7, a reference numeral190denotes an object of a matching target, and a reference numeral191denotes an inspection area set on a surface of the object190. The shape, size and arranged location on the object surface of the inspection area191are the same as those at the time of learning and at the time of registration. Further, inFIG.7, a reference numeral192denotes a grayscale image of the inspection area191captured by the camera110. The image192is configured of n by m pieces of pixels Gij (i=1, 2, . . . , m, j=1, 2, . . . , n) that are the same as those at the time of learning and at the time of registration. Further, inFIG.7, a reference numeral193denotes an individual identifier extracted from the image192by the extraction unit1705. Matching is performed on the individual identifier193with the entire individual identifiers stored in the database1607. As a result of matching, when the individual identifier193matches the individual identifier189stored in the database1607for example, a matching result indicating that the object190is a proper object is output. At that time, the surface property188stored in association with the individual identifier189is read out from the database1607, and is output together with the matching result. On the other hand, when the individual identifier193does not match any individual identifier stored in the database1607, a matching result indicating that the object190is a forged product is output.

As described above, according to the surface property estimation system100of the present embodiment, it is possible to estimate surface property from an image in which an object surface is captured and to extract the feature amount unique to the object, and record them in association with each other. This is because the present embodiment includes the image acquisition unit1703that acquires the image1604of a surface of an object, the estimation unit1704that estimates the surface property1605from the image1604by using the estimation model1603having been learned, the extraction unit1705that extracts the individual identifier1606that is a feature amount unique to the image from the image1604, and the registration unit1706that stores the estimated surface property1605and the extracted individual identifier1606in the database1607in association with each other.

Further, according to the present embodiment, at the time of performing matching using an individual identifier, when an object that is a properly registered one, it is possible to extract and output surface property stored in the database1607in association therewith at the time of registration. As a result, at the time of performing matching on a proper product, it is possible to confirm easily by using the surface property stored at the time of registration as an image.

Second Exemplary Embodiment

Next, a surface property estimation system200according to a second exemplary embodiment of the present invention will be described. As compared with the surface property estimation system100described with reference toFIG.1, the surface property estimation system200is the same as the surface property estimation system100except for the configuration of a training data creation unit1701.

FIG.8is a block diagram illustrating the training data creation unit1701in the surface property estimation system200. Referring toFIG.8, the training data creation unit1701includes a measurement unit17011, a correspondence table17012, an imaging condition acquisition unit17013, an imaging unit17014, and a generation unit17015.

The measurement unit17011is configured to measure the surface property of a predetermined area of an object surface by the measuring sensor120. In this example, the surface property is a three-dimensional point group data.

The correspondence table17012is a database in which the surface property of an object and an imaging condition are recorded in association with each other.FIG.9illustrates exemplary contents of the correspondence table17012. In this example, the correspondence table17012is configured of a plurality of entries, and each entry includes a surface property field and an imaging condition field. When there are “m” types of surface properties, the imaging condition database1153is configured of “m” entries, and each entry corresponds to one surface property type one to one. In this example, the surface property stored in each entry is a three-dimensional point group data.

In the imaging condition field of the correspondence table17012, an imaging condition P1specifying an imaging condition suitable for imaging the three-dimensional shape of an object surface specified by the surface property with high contract, or the like is recorded. As an imaging condition, an illumination angle may be used, for example. An illumination angle is an angle at which illumination light enters the surface of an object. As another example of an imaging condition, image resolution may be used. Image resolution is expressed in DPI (dot per inch), for example. Moreover, since image resolution and imaging magnification have a certain cause an effect relationship, imaging magnification may be used instead of image resolution. However, the imaging conditions are not limited to the examples described above. Other examples of imaging conditions include a distance between an object and a camera, intensity of illumination light, a wavelength of illumination light, magnitude of illumination, and the like. Imaging conditions specified by the imaging condition P1or the like may be one or a plurality of the imaging conditions provided above as examples. It is desirable that imaging conditions are learned in advance for each surface property type.

The imaging condition acquisition unit17013is configured to, on the basis of the surface property of an object measured by the measurement unit17011, acquire an imaging condition of the object from the correspondence table17012. Specifically, the imaging condition acquisition unit17013calculates the degree of approximation between the surface property of the measured object and the surface property recorded in each entry of the correspondence table17012. As a method of calculating the degree of approximation between surface properties, any method may be used if it can quantitatively compare the degrees of approximation between units of information. Then, the imaging condition acquisition unit17013acquires, from the correspondence table17012, an imaging condition recorded corresponding to surface property whose degree of approximation with the measured surface property is the highest (most approximate). Then, the imaging condition acquisition unit17013displays the acquired imaging condition on the screen display unit150to present it to an operator. As a result, an operator who performs imaging can easily recognize the imaging condition for imaging an object surface. In this example, the imaging condition acquisition unit17013is configured to display an acquired imaging condition on the display screen of the screen display unit150. However, the imaging condition acquisition unit17013may be configured to automatically set the imaging environment that conforms to the acquired imaging condition.

The imaging unit17014is configured to capture a predetermined area of an object surface by the camera110under the imaging condition.

The generation unit17015is configured to create the training data1602from an image of a predetermined area of an object surface captured by the imaging unit17014and the surface property of the predetermined area of the object surface measured by the measuring unit17011, and store it in the storage unit160.

Next, operation of the surface property estimation system200will be described. Operation of the surface property estimation system200is largely divided into a learning operation, a registration operation, and a matching operation. Among them, a registration operation and a matching operation are the same as those of the surface property estimation system100according to the first exemplary embodiment. Hereafter, details of a learning operation will be described.

<Learning Operation>

FIG.10is a flowchart of an example of a learning operation of the surface property estimation system200. Referring toFIG.10, the training data creation unit1701repeats steps S32to S35the defined number of times (steps S31, S36). The defined number of times is any number of times if it is larger than the number of times in which a required amount of training data is created. For example, when there are a plurality of objects for learning, the surface property estimation system200repeats steps S32to S35one or more times for each object for learning. At step S32, the training data creation unit1701measures the surface property of a predetermined area on the surface of an object for learning, by the measuring sensor120. At step S33, the training data creation unit1701acquires, from the correspondence table17012, an imaging condition stored in association with the surface property approximated to the surface property measured at step S32. At step S34, the training data creation unit1701images a predetermined area on a surface of an object for learning by the camera110, under the imaging environment determined by the acquired imaging condition. At step S35, the training data creation unit1701creates one unit of training data by associating the image acquired at step S34with the surface property acquired at step S32, and stores in the storage unit160.

Then, as similar to the case of the first exemplary embodiment, the learning unit1702uses the training data1602created by the training data creation unit1701to create, through machine learning, the estimation model1603in which an image of an object surface is an input and the surface property shown by the image is an output, and stores it in the storage unit160(step S37).

As described above, according to the present embodiment, the three-dimensional shape of a surface of an object for learning can be captured with high contrast. This is because the training data creation unit1701measures the surface property of a surface of an object by using the measuring sensor120, acquires, from the correspondence table17012, an imaging condition recorded in association with the surface property that is approximate to the measured surface property, and captures an image of the surface of the object under the acquired imaging condition.

As described above, according to the present embodiment, since the three-dimensional shape of a surface of an object for learning can be imaged with high contrast, the quality of the training data configured of the captured image of the object surface and the measured surface property is improved, resulting in an improvement in the estimation accuracy of the estimation model1603.

In the above description, in the surface property field for an object in each entry of the correspondence table17012, three-dimensional point group data of an object surface is stored. However, in the surface property field of each entry in the correspondence table17012, a parameter (for example Ra) of surface roughness that can be statistically calculated from three-dimensional point group data may be recorded, rather than three-dimensional point group data. In the case where correspondence between a parameter of surface roughness and an imaging condition is recorded in the correspondence table17012, the imaging condition acquisition unit17013may be configured to calculate a parameter of surface roughness from three-dimensional point group data of an object for learning measured by the measurement unit17011, and acquire, from the correspondence table17012, an imaging condition corresponding to a parameter of surface roughness approximate to the calculated parameter of surface roughness. Note that when the surface property of an object for learning measured by the measurement unit17011is a parameter of surface roughness originally, the calculation as described above is unnecessary.

Further, in the surface property field of each entry in the correspondence table17012, information about another characteristic of an object surface other than surface property may be recorded. Examples of information about other characteristics of an object surface include information representing the material of a surface object. Examples of information representing the material include metal, ceramic, resin, carbon fiber, glass, paper, wood, steel, and the like. Still another example of information about a characteristic of an object surface is a shape of an object (individual identification object) in a captured image. Exemplary shapes include a plane (rectangle), polygon, circle, ring (doughnut-shape), and the like. The shape may be a two-dimensional shape of a surface to be imaged. Moreover, the shape may be a shape of an image area of a surface of an object from which a feature amount is extracted. Other examples of data characterizing an object surface include reflectance of an object surface, transmittance, optical property, processing methods such as a satin process and a turning process, and the like. As described above, when another piece of information characterizing the object surface exists in the correspondence table, the imaging condition acquisition unit17013may acquire an imaging condition from the correspondence table while taking into account the other information characterizing the object surface according to the object for learning input from the operator via the operation input unit140.

Third Exemplary Embodiment

Next, a third exemplary embodiment of the present invention will be described with reference toFIG.11.FIG.11is a block diagram of a surface property estimation system300according to the present invention.

As illustrated inFIG.11, the surface property estimation system300according to the present embodiment is configured to include an image acquisition unit301, an estimation unit302, an extraction unit303, and a registration unit304.

The image acquisition unit301is configured to acquire an image of a surface of an object. The image acquisition unit301may have the same configuration as that of the image acquisition unit1703ofFIG.1, but it is not limited thereto.

The estimation unit302is configured to estimate the surface property from an image acquired by the image acquisition unit301, by using an estimation model obtained by means of machine learning using an image of a surface of an object and the surface property shown by the image as training data. The estimation unit302may have the same configuration as that of the estimation unit1704ofFIG.1for example, but is not limited thereto.

The extraction unit303is configured to extract, from the image acquired by the image acquisition unit301, a feature amount unique to the image. The extraction unit303may have the same configuration as that of the extraction unit1705ofFIG.1for example, but is not limited thereto.

The registration unit304is configured to store the surface property estimated by the estimation unit302and the feature amount extracted by the extraction unit303in a storage unit (not illustrated) in association with each other. The registration unit304may have the same configuration as that of the registration unit1706ofFIG.1for example, but is not limited thereto.

The surface property estimation system300configured as described above operates as described below. The image acquisition unit301acquires an image of a surface of an object. Then, the estimation unit302estimates the surface property from the image acquired by the image acquisition unit301, by using an estimation model that is obtained by machine learning with use of an image of a surface of an object and the surface property shown by the image as training data. Then, the extraction unit303extracts, from the image acquired by the image acquisition unit301, a feature amount unique to the image. Then, the registration unit304stores the surface property estimated by the estimation unit302and the feature amount extracted by the extraction unit303in a storage unit (not illustrated) in association with each other.

As described above, according to the present embodiment, it is possible to estimate the surface property from an image in which an object surface is captured and to extract the feature amount unique to the object, and record them in association with each other. This is because the present embodiment includes the image acquisition unit301that acquires an image of a surface of an object, the estimation unit302that estimates the surface property from the image acquired by the image acquisition unit301by using an estimation model obtained by machine learning with use of an image of a surface of an object and the surface property shown by the image as training data, the extraction unit303that extracts, from the image acquired by the image acquisition unit301, the feature amount unique to the image, and the registration unit304that stores the surface property estimated by the estimation unit302and the feature amount extracted by the extraction unit303in a storage unit (not illustrated) in association with each other.

While the present invention has been described with reference to the exemplary embodiments described above, the present invention is not limited to the above-described embodiments. The form and details of the present invention can be changed within the scope of the present invention in various manners that can be understood by those skilled in the art. For example, configurations as described below are also included in the present invention.

INDUSTRIAL APPLICABILITY

The present invention is applicable to any systems for measuring surface property of an object such as an industrial product and recording it in association with an object identifier.

The whole or part of the exemplary embodiments disclosed above can be described as, but not limited to, the following supplementary notes.

(Supplementary Note 1)

A surface property estimation system comprising:

image acquisition means for acquiring an image of a surface of an object;

estimation means for estimating a surface property from the acquired image by using an estimation model obtained through machine learning with use of an image of a surface of an object and a surface property shown by the image as training data;

extraction means for extracting, from the acquired image, a feature amount unique to the image; and

registration means for storing the estimated surface property and the extracted feature amount in a storage means in association with each other.

(Supplementary Note 2)

The surface property estimation system according to supplementary note 1, further comprising

training data generation means, wherein

the training data generation means includes:

measuring means for measuring the surface property of the object by using a measuring sensor;

a correspondence table in which a surface property and an imaging condition are associated with each other;

imaging condition acquisition means for calculating a degree of approximation between the measured surface property and the surface property recorded in the correspondence table, and acquiring an imaging condition from the correspondence table on a basis of the calculated degree of approximation;

capturing means for capturing an image of a surface of an object under the acquired imaging condition; and

generation means for generating the training data from the captured image and the measured surface property.

(Supplementary Note 3)

The surface property estimation system according to supplementary note 1 or 2, further comprising

matching means for performing matching between the extracted feature amount and the feature amount stored in the storage means, and outputting a surface property stored in the storage means in association with a feature amount that conforms to the extracted feature amount.

(Supplementary Note 4)

The surface property estimation system according to any of supplementary notes 1 to 3, wherein the surface property is three-dimensional point group data.

(Supplementary Note 5)

The surface property estimation system according to any of supplementary notes 1 to 3, wherein the surface property is a parameter of surface roughness.

(Supplementary Note 6)

A surface property estimation method comprising:

acquiring an image of a surface of an object;

estimating a surface property from the acquired image by using an estimation model obtained through machine learning with use of an image of a surface of an object and a surface property shown by the image as training data;

extracting, from the acquired image, a feature amount unique to the image; and

storing the estimated surface property and the extracted feature amount in a storage means in association with each other.

(Supplementary Note 7)

The surface property estimation method according to supplementary note 6, further comprising generating the training data, wherein the generating the training data includes:

measuring the surface property of the object by using a measuring sensor;

calculating a degree of approximation between the measured surface property and a surface property recorded in a correspondence table in which the surface property and an imaging condition are associated with each other, and acquiring an imaging condition from the correspondence table on a basis of the calculated degree of approximation;

capturing an image of a surface of an object under the acquired imaging condition; and

generating the training data from the captured image and the measured surface property.

(Supplementary Note 8)

A computer-readable storage medium storing thereon a program for causing a computer to execute processing to:

acquire an image of a surface of an object;

estimate a surface property from the acquired image by using an estimation model obtained through machine learning with use of an image of a surface of an object and a surface property shown by the image as training data;

extract, from the acquired image, a feature amount unique to the image; and

store the estimated surface property and the extracted feature amount in a storage means in association with each other.

REFERENCE SIGNS LIST

100surface property estimation system110camera120measuring sensor130communication I/F unit140operation input unit150screen display unit160storage unit1601program1602training data1603estimation model1604image1605surface property1606individual identifier1607database170arithmetic processing unit1701training data creation unit17011measurement unit17012correspondence table17013imaging condition acquisition unit17014imaging unit17015generation unit1702learning unit1703image acquisition unit1704estimation unit1705extraction unit1706registration unit1707matching unit181object182inspection area183image184surface property185object186inspection area187image188surface property189individual identifier190object191inspection area192image193individual identifier200surface property estimation system300surface property estimation system301image acquisition unit302estimation unit303extraction unit304registration unit