Categorical Color Perception System
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Abstract
The present invention relates to a categorical color perception system which automatically judges a categorical color and aims to judge a categorical color name correctly under various ambient lights. Test color measured at an experiment is inputted to an input layer portion corresponding to test color components 101, illumination light components at the experiment are inputted to an input layer portion corresponding to illumination light components 102, and connection weights are obtained by learning with backpropagation method so as to output a categorical color judged by an examinee. Although a structure between the input layer portion corresponding to test color components 101 and the input-side hidden layer portion corresponding to test color components 103 and a structure between the input layer portion corresponding to illumination light components 102 and the input-side hidden layer portion corresponding to illumination light components 104 are independent, weights of structurally corresponding connections are made the same.
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Citations
21 Claims
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1-11. -11. (canceled)
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12. A categorical color perception system inputting components of ambient light under a judgment environment and components of a reflected color by an object to be judged under the judgment environment and outputting a categorical color that is a categorized color name which is predicted to be perceived by an observer with the object to be judged under the judgment environment, the categorical color perception system comprising:
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(1) a connection weights for judgment data memory unit storing connection weights obtained by learning in a neural network for learning, wherein the neural network for learning has at least four layers of an input layer, an input-side hidden layer, an output-side hidden layer provided between the input-side hidden layer and an output layer, and the output layer, wherein the input layer comprises an input layer portion corresponding to illumination light components for inputting components of illumination light under a experimental environment and an input layer portion corresponding to test color components for inputting components of a test color that is reflection of the illumination light by a color sample, wherein the input layer portion corresponding to illumination light components and the input layer portion corresponding to test color components comprise a same number of units for inputting color components in a same method, wherein the input-side hidden layer comprises an input-side hidden layer portion corresponding to illumination light components which is not connected to the input layer portion corresponding to test color components but connected to the input layer portion corresponding to illumination light components and an input-side hidden layer portion corresponding to test color components which is not connected to the input layer portion corresponding to illumination light components but connected to the input layer portion corresponding to test color components, wherein the input-side hidden layer portion corresponding to illumination light components and the input-side hidden layer portion corresponding to test color components comprise a same number of units, wherein the output-side hidden layer is connected to the input-side hidden layer portion corresponding to illumination light components and the input-side hidden layer portion corresponding to test color components, wherein the output layer corresponds to categorical colors; wherein the neural network for learning uses connection weights shared by structurally corresponding connections for connection weights with respect to connections between the input layer portion corresponding to illumination light components and the input-side hidden layer portion corresponding to illumination light components and connection weights with respect to connections between the input layer portion corresponding to test color components and the input-side hidden layer portion corresponding to test color components; and wherein the connection weights stored are obtained with a backpropagation method in which the neural network for learning inputs components of illumination light color for learning and components of test color for learning and output a categorical color for learning perceived by an examinee from the color sample under the illumination light; and (2) a neural network for judgment having a same structure with the neural network for learning, wherein the neural network for judgment inputs the components of the ambient light of the judgment environment as the components of the illumination light color and inputs the components of the reflected color by the object to be judged under the judgment environment as the components of the test color; wherein the neural network for judgment uses connection weights shared by structurally corresponding connections for connection weights with respect to connections between the input layer portion corresponding to illumination light components and the input-side hidden layer portion corresponding to illumination light components and connection weights with respect to connections between the input layer portion corresponding to test color components and the input-side hidden layer portion corresponding to test color components; and wherein the neural network for judgment carries out a neural network operation process according to the connection weights stored in the connection weights for judgment data memory unit, and outputs the categorical color predicted to be perceived by the observer with the object to be judged under the judgment environment as a processed result. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification