WO2021210413A1 - Procédé de classification de cibles de discrimination, programme utilisé pour le procédé et dispositif de discrimination - Google Patents

Procédé de classification de cibles de discrimination, programme utilisé pour le procédé et dispositif de discrimination Download PDF

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WO2021210413A1
WO2021210413A1 PCT/JP2021/014130 JP2021014130W WO2021210413A1 WO 2021210413 A1 WO2021210413 A1 WO 2021210413A1 JP 2021014130 W JP2021014130 W JP 2021014130W WO 2021210413 A1 WO2021210413 A1 WO 2021210413A1
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feature
multidimensional
multidimensional feature
feature vector
discrimination
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PCT/JP2021/014130
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Japanese (ja)
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貴之 石黒
星野　仁
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株式会社Ｒｏｘｙ
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

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  • the present invention relates to a method for classifying discriminating objects, a program used for the classification method, and a discriminating device.
  • Patent Document 1 uses a convolutional neural network to perform a calculation by a convolutional layer on an image to be discriminated, and features from the first fully connected layer after the calculation of the convolutional layer is completed.
  • a method of extracting a quantity (multidimensional feature vector), executing a multivariate analysis using the extracted multidimensional feature vector, and classifying the discrimination target based on the result of the multivariate analysis is described.
  • the multidimensional feature vector extracted from the first fully connected layer after the calculation of the convolution layer is completed is not always effective as a feature quantity used for classification of the discrimination target, and in terms of improving the classification accuracy, it is still possible. There is room for improvement.
  • the present invention has been made in view of the above, and one of the objects of the present invention is to provide a technique that contributes to improvement of classification accuracy.
  • the classification method of the discrimination target (a) the data of the discrimination target is acquired, and (b) a feature extractor using a neural network including deep learning is used to obtain the data of the discrimination target. A one-dimensional feature vector is extracted, and (c) the extracted first multidimensional feature vector is subjected to a second multidimensional lower dimension than the first multidimensional feature vector by a feature converter using a plurality of fully connected layers. It is converted into a feature vector, and (d) based on the converted second multidimensional feature vector, the discrimination target is classified by a classifier using statistical machine learning.
  • the "data to be discriminated” in the present invention typically corresponds to the image data to be discriminated, and the image data to be discriminated literally corresponds to this, but has been learned. In the learning stage of the model, it is a concept that includes sample data with a teacher signal.
  • the "statistical machine learning device” in the present invention refers to machine learning other than machine learning using a neural network including deep learning, such as gradient boosting, support vector machine, random forest, neural network, and Gaussian normalization. , Sansamble inspection, etc. correspond to this.
  • the classification of the discrimination target is accurate. It can be realized well. It should be noted that a feature extractor for extracting the first multidimensional feature vector, which is a variety of feature quantities of the data to be discriminated, and a plurality of full couplings for converting the first multidimensional feature vector into a low-dimensional second multidimensional feature vector. Since the feature converter using the layer and the classifier that classifies the discrimination target based on the second multidimensional feature vector converted by the feature converter have different configurations, it is necessary to improve the classification accuracy of the discrimination target.
  • Learning of feature extractors and feature converters and learning of classifiers can be performed separately. That is, when the cause of the low classification accuracy is the low feature extraction / conversion accuracy by the feature extractor and the feature converter, the feature extractor and the feature converter are trained and the classification accuracy by the classifier is performed. If it is caused by the low level, only the classifier can be trained, and the classification accuracy can be improved efficiently.
  • a program for classifying the discrimination target is configured.
  • the program is for causing one or a plurality of computers to execute each step of the classification method of the discrimination target according to the present invention in any of the above-described aspects.
  • the program may be recorded on a computer-readable recording medium such as a hard disk, ROM, SSD, flash memory (USB memory, SD card, etc.), floppy disk, CD, DVD, or a transmission medium.
  • a computer-readable recording medium such as a hard disk, ROM, SSD, flash memory (USB memory, SD card, etc.), floppy disk, CD, DVD, or a transmission medium.
  • a communication network such as the Internet or LAN, or may be exchanged in any other manner.
  • each step of the discrimination target classification method according to the present invention in any of the above-described embodiments. Is executed, so that the same action and effect as the above-described method for classifying the discrimination target according to the present invention, for example, the effect that the classification of the discrimination target can be accurately realized can be obtained.
  • a data acquisition unit for acquiring data to be discriminated and a neural network including deep learning for extracting a first multidimensional feature vector from the acquired data to be discriminated are used.
  • a feature extractor and a plurality of fully connected layers that convert the first multidimensional feature vector extracted by the feature extractor into a second multidimensional feature vector having a lower dimension than the first multidimensional feature vector are used. It includes a feature converter and a classifier using statistical machine learning that classifies discrimination targets using a second multidimensional feature vector.
  • the "data to be discriminated" in the present invention typically corresponds to the image data to be discriminated, and the image data to be discriminated literally corresponds to this, but feature extraction.
  • the "statistical machine learning device" in the present invention refers to machine learning other than machine learning using a neural network including deep learning, such as gradient boosting, support vector machine, random forest, neural network, and Gaussian normalization. , Sansamble inspection, etc. correspond to this.
  • the classification of the discrimination target is accurate. It can be realized well.
  • a feature extractor that extracts the first multidimensional feature vector, which is a variety of feature quantities of the data to be discriminated, and a plurality of all that convert the first multidimensional feature vector into a low-dimensional second multidimensional feature vector. Since the feature converter using the coupling layer and the classifier that classifies the discrimination target based on the second multidimensional feature vector converted by the feature converter have different configurations, the classification accuracy of the discrimination target is improved.
  • the learning of the feature extractor and the feature converter, and the learning of the classifier can be carried out separately. That is, when the cause of the low classification accuracy is the low feature extraction accuracy by the feature extractor and the feature converter, the feature extractor and the feature converter are trained and the classification accuracy by the classifier is low. If this is the case, only the classifier can be trained, and the classification accuracy can be improved efficiently.
  • a storage unit that stores the second multidimensional feature vector and a distance value between each of the plurality of second multidimensional feature vectors stored in the storage unit are stored. It also has a calculation department for calculation. Then, in the learning stage, the feature extractor and the feature converter learn by using the error backpropagation method and the gradient descent method based on the distance value calculated by the calculation unit.
  • the classification of the discrimination target is effective, and the classification of the discrimination target is performed using the second multidimensional feature vector extracted and converted in the learning stage of the feature extractor and the feature converter. Therefore, the classification accuracy is correct. Can be further improved.
  • the classification accuracy can be improved.
  • the computer 1 that functions as the discrimination device according to the present embodiment is configured as a microprocessor centered on the CPU 2, and has a ROM 4 that stores various processing programs and temporarily stores data.
  • RAM 6, GPU 8 that performs calculation processing and matrix calculation processing necessary for performing image processing hard disk (HDD) 10, which is a large-capacity memory that stores various data including various application programs (simply called applications) and image data, It is provided with an input / output interface (I / F) 12 for inputting / outputting data to / from an external device such as a camera 70.
  • I / F input / output interface
  • the computer 1 as the discrimination device according to the present embodiment will be described as a device that discriminates whether the discrimination target is an OK product or an NG product.
  • the computer 1 is an example of an implementation configuration corresponding to the "discrimination device" in the present invention.
  • the computer 1 is provided with an input device 14 such as a keyboard and a mouse for which a user inputs various commands, a display 60 for displaying various information, and the like.
  • the CPU 2, ROM 4, RAM 6, GPU 8, HDD 10, I / F 12, input device 14, display 60, and the like are electrically connected by a bus 80 so that various control signals and data can be exchanged with each other.
  • the computer 1 has a function of executing an operation corresponding to the input operation when the user inputs the cursor or the like displayed on the display 60 via the input device 14. Further, the computer 1 functions as a discrimination device according to the present embodiment by executing various processes by an application stored in the HDD 10, specifically, a discrimination application that executes the discrimination processing of the discrimination target. .. In the present embodiment, the discriminating device is configured to be feasible by the computer 1, but it may be realized as a dedicated device.
  • the computer 1 includes the above-mentioned hardware resources such as CPU2, ROM4, RAM6, GPU8, HDD10, I / F12, input device 14, and display 60, a discrimination application, and the present embodiment.
  • Image display control unit 20, image acquisition unit 22, area designation unit 24, feature extractor 26, feature converter 27, classifier 28, distance calculation in collaboration with software such as the visualization program related to The device 29, the two-dimensional graphed data generation unit 30, the storage unit 33, and the like are configured as functional blocks. In other words, each of these parts (image display control unit 20, image acquisition unit 22, area designation unit 24, feature extractor 26, feature converter 27, classifier 28, distance calculator 29, two-dimensional graphing data generation unit).
  • the feature extractor 26, the feature converter 27, and the display 60 are examples of an implementation configuration corresponding to the “visualization device” in the present invention.
  • the image display control unit 20 displays a predetermined window 62 on the screen 61 of the display 60 as shown in FIG. Further, the image display control unit 20 teaches based on the sample image data 34a with a teacher signal (see FIG. 2) selected by the user inputting the cursor or the like displayed on the display 60 via the input device 14.
  • a sample image with a signal is displayed on the window 62
  • a discrimination target image is displayed on the window 62 based on the discrimination target image data 34b (see FIG. 2) acquired by the image acquisition unit 22, and the area designation unit 24 is displayed.
  • the designated area image is displayed on the window 62 based on the designated area image data 34c (see FIG. 2) acquired by.
  • the image display control unit 20 displays a two-dimensional graph on the window 62 based on the model two-dimensional graphed data 36a (see FIG. 2) stored in the storage unit 33, or generates two-dimensional graphed data.
  • the discrimination two-dimensional graph is displayed on the window 62 based on the discrimination two-dimensional graphing data 36b generated by the unit 30. Further, the image display control unit 20 displays the discrimination result by the classifier 28 in the window 62.
  • the sample image with a teacher signal is an image used for learning the feature extractor 26 and the feature converter 27.
  • the discrimination target image typically corresponds to an image of a target whose classification (OK product or NG product) is unknown, but verification and learning of the learning results of the feature extractor 26 and the feature converter 27
  • it preferably includes an image of a target whose classification (OK product or NG product) is known, for example, a sample image with a teacher signal arbitrarily selected by the user from a plurality of sample images with a teacher signal.
  • the image acquisition unit 22 acquires sample image data 34a with a teacher signal and image data 34b to be discriminated taken by the camera 70 (see FIG. 1), and uses these acquired image data as the image display control unit 20 and the feature extractor 26. , Supply to the storage unit 33 and the like.
  • the area designation unit 24 is used when an arbitrary area of the sample image with a teacher signal or the image to be discriminated displayed on the window 62 is clicked or dragged by an input operation via the user's input device 14.
  • 34c of the image data (hereinafter referred to as “designated area image data”) 34c is acquired, and the acquired designated area image data 34c (see FIG. 2) is supplied to the image display control unit 20, the feature extractor 26, the storage unit 33, and the like. ..
  • the feature extractor 26 extracts the first multidimensional feature vectors 90a, 90b, 90c from the plurality of sample image data 34a with teacher signals, the discriminant target image data 34b, and the designated area image data 34c, and extracts the first multidimensional.
  • the feature vectors 90a, 90b, 90c are supplied to the feature converter 27.
  • the feature extractor 26 is configured to use a convolutional neural network (CNN) as shown in FIGS. 3 and 4.
  • CNN convolutional neural network
  • the first multidimensional feature vectors 90a, 90b, and 90c are examples of implementation configurations corresponding to the "first multidimensional feature vector" in the present invention, respectively.
  • a plurality of sample image data 34a with teacher signals and discrimination targets are used using a predetermined filter (not shown).
  • the so-called convolution process for extracting the features of the image data 34a, 34b, 34c from the image data 34b and the designated area image data 34c without losing the features is performed a plurality of times, and then flattened into a column vector.
  • the feature converter 27 has a plurality of fully connected layers 27a and 27b, and the first multidimensional extracted by the feature extractor 26 using the fully connected layers 27a and 27b.
  • the process of lowering the dimension of the feature vectors 90a, 90b, 90c is executed.
  • the first multidimensional feature vectors 90a, 90b, 90c are converted into the first multidimensional feature vectors 90a, 90b, 90c by Fully Connected processing, and the second multidimensional feature vectors 92a, which are lower in dimension than the first multidimensional feature vectors 90a, 90b, 90c, It is converted into 92b and 92c and supplied to the classifier 28, the distance calculator 29, the two-dimensional graphing data purification unit 30, and the storage unit 33.
  • the first multidimensional feature vectors 90a, 90b, 90c are obtained from the first multidimensional feature vectors 90a, 90b, 90c by pooling processing such as Global max Polling or Global Average Polling instead of the Fully Connected processing. May be converted into low-dimensional second multidimensional feature vectors 92a, 92b, 92c.
  • the second multidimensional feature vectors 92a, 92b, and 92c are stored in the storage unit 33, respectively.
  • the second multidimensional feature vectors 92a, 92b, and 92c are examples of the implementation configurations corresponding to the "second multidimensional feature vector" in the present invention.
  • the feature extractor 26 and the feature converter 27 include the first multidimensional feature vectors 90a, 90b, 90c and the second multidimensional feature vectors 92a, 92b, which can accurately discriminate the classification of the discrimination target. Learning is performed in advance so that 92c can be obtained, and the learned model 35 is stored in the storage unit 33. Twice
  • the classifier 28 functions when discriminating the classification of the discriminating target.
  • the classification of the discrimination target that is, whether the discrimination target is an OK product or an NG product, is based on the second multidimensional feature vectors 92a, 92b, 92c converted by the feature converter 27. Is determined, and the determination result is supplied to the image display control unit 20.
  • the classification of the discrimination target is that the second multidimensional feature vectors 92a, 92b, 92c to be discriminated by the arithmetic parameters machine-learned using the second multidimensional feature vectors 92a, 92b, 92c are OK products.
  • a one-dimensional numerical value indicating the degree of certainty is calculated, and if it is equal to or more than the set threshold value, it is classified as an OK product, and if it is less than the set threshold value, it is classified as an NG product.
  • the classifier 28 is configured to use so-called statistical machine learning such as gradient boosting, support vector machine, random forest, neural network, Gaussian normalization, and ensemble inspection.
  • the distance calculator 29 functions when generating the trained model 35, and when verifying the trained model 35 and improving the accuracy.
  • the distance calculator 29 calculates the distance between the second multidimensional feature vectors 92a of the sample image data 34a with a plurality of teacher signals, and uses the calculated distance as the distance data 38. It is stored in the storage unit 33. The calculated distance is fed back to the feature extractor 26 and the feature converter 27 (see FIG. 4A).
  • the distance calculator 29 uses the second multidimensional feature vector 92a of the sample image data 34a with a plurality of teacher signals, the discriminant image data 34b, and the designation. The distance between the second multidimensional feature vectors 92b and 92c of the area image data 34c is calculated, and the calculated distance is stored in the storage unit 33 as the distance data 38.
  • the feedback to the feature extractor 26 and the feature converter 27 is the distance between the second multidimensional feature vectors 92a between the OK products and the second multidimensional feature vector 92a of the OK product and the NG product.
  • the configuration was made by modifying the parameters of the vessel 27. In such parameter modification (learning of the feature extractor 26 and the feature converter 27), the second multidimensional feature vector 92a is visualized (two-dimensional graphing), and the trained model 35 (feature extractor 26 and the feature converter 27) is modified.
  • the trained model 35 is generated and stored in the storage unit 33.
  • the learning of the trained model 35 and the learning of the feature extractor 26 and the feature converter 27 are synonymous.
  • deep metric learning is used for modifying the parameters of the feature extractor 26 and the feature converter 27. That is, the first multidimensional feature vector 90a is extracted by the feature extractor 26, and the extracted first multidimensional feature vector 90a is converted into the second multidimensional feature vector 92a by the feature converter 27, and the converted second multi.
  • the two-dimensional graphing data generation unit 30 generates the model two-dimensional graphing data 36a based on the second multidimensional feature vector 92a of the plurality of sample image data 34a with teacher signals, and also generates the discrimination target image data 34b and Based on the second multidimensional feature vectors 92b and 92c of the designated area image data 34c, the discriminant two-dimensional graphed data 36b is generated.
  • the two-dimensional graphing data 36a for the model and the two-dimensional graphing data 36b for discrimination are generated by using the second multidimensional feature vectors 92a, 92b, and 92c as a set of the feature quantities of the number of dimensions.
  • the second multidimensional feature vectors 92a, 92b, 92c are n-dimensional column vectors (f 1 , f 2 , f 3 , ..., f n-1 , f n ), n feature quantities f.
  • Data as a set of 1 , f 2 , f 3 , ..., f n-1 , f n , that is, as shown in FIG.
  • the sample image data 34a with a plurality of teacher signals stored in the storage unit 33 by the user For the purpose of verifying the learning result of the trained model 35 (feature extractor 26 and feature converter 27) and improving the learning accuracy, the sample image data 34a with a plurality of teacher signals stored in the storage unit 33 by the user.
  • the two-dimensional graphing data generation unit 30 has the arbitrary one teacher signal from the model two-dimensional graphed data 36a.
  • a model generated based on the second multidimensional feature vector data 92a hereinafter referred to as "arbitrary second multidimensional feature vector 92a'" of the sample image data 34a (hereinafter referred to as "arbitrary sample image data 34a'").
  • Two-dimensional graphing data 36a for use hereinafter, referred to as "selected two-dimensional graphing data 36a'" is extracted.
  • the storage unit 33 is secured in at least one of the RAM 6 and the HDD 10, and is the sample image data 34a with a teacher signal taken by the camera 70 (see FIG. 1), the image data 34b to be discriminated, and the designation acquired by the area designation unit 24.
  • the multidimensional feature vectors 92a, 92b, 92c and the distance data 38 calculated by the distance calculator 29 are stored.
  • FIG. 6 is a main flowchart showing an example of the classification determination routine.
  • the feature extractor 26 (trained feature extractor 26) using the trained model 35 performs the discrimination target image data 34b, the arbitrary sample image data 34a', or the designated area.
  • Image data 34c is read (step S10), and a process of extracting the first multidimensional feature vectors 90a, 90b, 90c from the read discrimination target image data 34b, arbitrary sample image data 34a', or designated area image data 34c is executed. (Step S12).
  • the feature converter 27 (trained feature converter 27) using the trained model 35 uses the first multidimensional feature vectors 90a, 90b, 90c extracted by the feature extractor 26 as the second multidimensional feature vector.
  • the process of converting to 92a, 92b, 92c is executed (step S14).
  • the classifier 28 uses the second multidimensional feature vectors 92a, 92b, 92c converted by the feature converter 27 to discriminate not only the image of the target whose classification is unknown but also the arbitrary sample image.
  • the process of classifying the images (designated area images) of arbitrary areas of these images specified by the user is executed (step S16), and the discrimination result (OK product or NG product) according to the classification is output. (Step S18), this routine is terminated.
  • the first multidimensional feature vectors 90a, 90b, 90c are converted into the second multidimensional feature vectors 92a, 92b, 92c by the feature converter 27 learned in advance using deep metric learning, and thus discriminated. It is possible to reduce the dimension without losing the features that are effective for classifying the object.
  • the learning of the trained model 35 (feature extractor 26 and feature converter 27) using deep metric learning is visualized and the trained model 35 (feature extractor 26 and feature conversion) is performed.
  • the second multidimensional feature vector 92a which is effective for classifying the discrimination target, is performed until the learning situation (individuality) of the vessel 27) is visually confirmed and the learning situation (individuality) becomes a desired state. It can be appropriately converted to 92b and 92c.
  • FIG. 7A is an explanatory diagram showing a state in which the trained model 35 is in an appropriate learning situation (individuality), and FIG. 7B is a state in which the learning situation (individuality) of the trained model 35 is not appropriate.
  • the reference numeral "Og” is a graph generated by the two-dimensional graphing data generation unit 30 based on the second multidimensional feature vector 92a of the sample image data 34a with a plurality of teacher signals of the OK product.
  • the symbol “Ng” is a graph generated by the two-dimensional graphing data generation unit 30 based on the second multidimensional feature vector 92a of the sample image data 34a with a plurality of teacher signals of the NG product.
  • 7 (a) and 7 (b) are one-dimensional numerical indexes such as a correct answer rate, a precision rate, and a recall rate that are generally used when evaluating the learning status (individuality) of the trained model 35. All are visualizations of the trained model 35 which is the same or has similar numerical indexes.
  • the trained model 35 when the trained model 35 is in an appropriate learning situation (individuality), the two-dimensional graph Og for the model and the two-dimensional graph Ng for the model are displayed clearly separated, and the boundary is displayed.
  • the second multidimensional feature vectors 92a, 92b, 92c, which are effective for classifying the discrimination target, are appropriate by using the feature converter 27 using the trained model 35, which is clarified and the learning situation (individuality) is appropriate. Can be obtained.
  • the two-dimensional graph Og for the model and the two-dimensional graph Ng for the model are displayed close to each other, and the boundary becomes unclear. If the feature converter 27 using the trained model 35 whose situation (individuality) is not appropriate is used, the second multidimensional feature vectors 92a, 92b, 92c effective for classifying the discrimination target cannot be appropriately obtained.
  • the trained model 35 By visualizing the learning status (individuality) of the trained model 35 in this way, it is possible to evaluate whether or not the trained model 35 is in a learning status (individuality) that can appropriately discriminate the classification of the discrimination target.
  • An appropriate trained model 35 can be generated with a smaller amount of sample data than in the past. Then, by being able to generate an appropriate trained model 35, it is possible to widen the setting range of the threshold value appropriate for the classification of the discrimination target by the classifier 28. As a result, it is possible to suppress erroneous discrimination due to disturbance, for example, the influence of external light when acquiring an image to be discriminated, and it is possible to stably obtain an accurate discriminant result.
  • the second multidimensional feature vector 92b to be discriminated in addition to the two-dimensional graphs Og and Ng for the model, the second multidimensional feature vector 92b to be discriminated and the second multidimensional feature of the region specified by the user. Since the state of the vector 92c is displayed as a two-dimensional graph Dg for discrimination, it is possible to visualize the discrimination status of the discrimination target and the classification of the designated area. As a result, it is possible to visually confirm the discrimination status of the discrimination target and the discrimination status of the portion of concern by the user (for example, when there is a scratch on the discrimination target, the scratch can be discriminated as NG.
  • the learning of the feature extractor 26 and the feature converter 27, or the learning of the classifier 28 can be redone.
  • the classifier 28 classifies the discrimination target using the second multidimensional feature vectors 92a, 92b, 92c having features effective for the classification of the discrimination target, the classification of the discrimination target can be realized with high accuracy.
  • the classification of the discrimination target by the classifier 28 is performed by the second multidimensional of the discrimination target based on the calculation parameters machine-learned using the plurality of second multidimensional feature vectors 92a stored in the storage unit 33.
  • a one-dimensional numerical value indicating the certainty that the feature vectors 92b and 92c are OK products is calculated and compared with the set threshold value.
  • the classification of the discrimination target is performed.
  • the cause of the erroneous determination can be easily identified, and the countermeasure can be appropriately taken.
  • the shapes of the discriminant two-dimensional graph Dg and the selected two-dimensional graph Cg are separated from the model two-dimensional graph Ng, and it is determined that the product is NG despite the different shapes.
  • the shapes of the discriminant two-dimensional graph Dg and the selected two-dimensional graph Cg are different from the model two-dimensional graph Og, and despite the different shapes, the product is OK.
  • the two-dimensional graph Dg for discrimination is a graph generated by the two-dimensional graphing data generation unit 30 based on the second multidimensional feature vectors 92b and 92c of the discrimination target image data 34b and the designated area image data 34c. ..
  • the selected two-dimensional graph Cg is a graph generated by the two-dimensional graphing data generation unit 30 based on the selected two-dimensional graphing data 36a'.
  • the first sample image data 34a with a plurality of teacher signals, the discrimination target image data 34b, and the designated area image data 34c are used by using the trained feature extractor 26.
  • the multidimensional feature vectors 90a, 90b, 90c are extracted, and the extracted first multidimensional feature vectors 90a, 90b, 90c are used with the trained feature converter 27 to obtain the first multidimensional feature vectors 90a, 90b, 90c.
  • the second multidimensional feature vectors 92a, 92b, 92c which are lower in dimension and effective for classifying the discrimination target, and to classify the discrimination target based on the converted second multidimensional feature vectors 92a, 92b, 92c.
  • the classification of the discrimination target can be realized with high accuracy.
  • the first sample image data 34a with a plurality of teacher signals to be discriminated, the discriminant target image data 34b, and the designated area image data 34c have various feature quantities.
  • the feature extractor 26 for extracting the multidimensional feature vectors 90a, 90b, 90c and the first multidimensional feature vectors 90a, 90b, 90c are converted into the low-dimensional second multidimensional feature vectors 92a, 92b, 92c with the feature converter 27. Since the classifier 28 that classifies the discrimination target based on the second multidimensional feature vectors 92a, 92b, 92c converted by the feature converter 27 has a different configuration, the feature is improved in improving the classification accuracy of the discrimination target.
  • Learning of the extractor 26 and the feature converter 27 and learning of the classifier can be carried out separately. That is, when the cause of the low classification accuracy is the low feature extraction / conversion accuracy by the feature extractor 26 and the feature converter 27, the feature extractor 26 and the feature converter 27 are trained and classified. When it is caused by the low classification accuracy of the device 28, only the classifier 28 can be trained, and the classification accuracy can be efficiently improved.
  • the discriminating device has been described as a device that discriminates whether the discriminating target is an OK product or an NG product, but the present invention is not limited to this.
  • the discriminating device may be applied to a device that discriminates which of a plurality of classifications the discriminating target is classified into.
  • the vertical axis has column numbers 1, 2, 3, ..., N-1, n
  • the horizontal axis is the feature quantity.
  • a coordinate system Cartesian coordinate system
  • these feature quantities f 1 , f 2 , f 3 , ..., f n A two-dimensional graph in which -1 and f n are plotted and connected by a curve or a straight line is used, but the present invention is not limited to this.
  • the input device 14 has been described as a keyboard and a mouse, but may include a pointing device such as a touch panel, a button, a dial, a touch sensor, a touch pad, and the like.
  • the large-capacity memory for storing various data including image data is HDD 10, but the present invention is not limited to this.
  • a large-capacity memory for storing various data including image data a flash memory (USB memory, SD card, etc.), SSD, floppy disk, CD, DVD, or the like may be applied.
  • various applications including a visualization program are stored in the HDD 10, but the present invention is not limited to this.
  • various applications including a visualization program may be configured to be distributed from another computer to the computer 1 as a discrimination device according to the present embodiment via a transmission medium, for example, a communication network such as the Internet or LAN.
  • the present embodiment shows an example of a mode for carrying out the present invention. Therefore, the present invention is not limited to the configuration of the present embodiment.

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Abstract

Le problème posé par l'invention est d'améliorer la précision de classifications. La solution porte sur des premiers vecteurs multidimensionnels de caractéristiques (90a, 90b, 90c) extraits d'une pluralité d'ensembles de données d'images d'échantillons liées à un signal d'enseignement (34a), de données d'images cibles de discrimination (34b) ou de données d'images de zone spécifiée (34c), à l'aide d'un extracteur de caractéristiques instruit (26). Les premiers vecteurs multidimensionnels de caractéristiques extraits (90a, 90b, 90c) sont convertis, à l'aide d'un convertisseur de caractéristiques instruit (27), en seconds vecteurs multidimensionnels de caractéristiques (92a, 92b, 92c) comptant moins de dimensions que les premiers vecteurs multidimensionnels de caractéristiques (90a, 90b, 90c) et servant à classifier une cible de discrimination, tandis que la classification de la cible de discrimination a lieu en fonction des seconds vecteurs multidimensionnels convertis de caractéristiques (92a, 92b, 92c). La classification de la cible de discrimination peut donc s'effectuer à haute précision.
PCT/JP2021/014130 2020-04-17 2021-04-01 Procédé de classification de cibles de discrimination, programme utilisé pour le procédé et dispositif de discrimination WO2021210413A1 (fr)

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WO2018170421A1 (fr) * 2017-03-17 2018-09-20 Magic Leap, Inc. Procédés et techniques d'estimation de disposition de pièce
JP2019507935A (ja) * 2016-03-11 2019-03-22 エヌイーシー ラボラトリーズ アメリカ インクＮＥＣ Ｌａｂｏｒａｔｏｒｉｅｓ Ａｍｅｒｉｃａ， Ｉｎｃ． 物体ランドマーク検出のための深層変形ネットワーク
JP2020060879A (ja) * 2018-10-05 2020-04-16 オムロン株式会社 学習装置、画像生成装置、学習方法、及び学習プログラム

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JP2019507935A (ja) * 2016-03-11 2019-03-22 エヌイーシー ラボラトリーズ アメリカ インクＮＥＣ Ｌａｂｏｒａｔｏｒｉｅｓ Ａｍｅｒｉｃａ， Ｉｎｃ． 物体ランドマーク検出のための深層変形ネットワーク
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