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Color space transition method based on fuzzy logic and neural network

A technology of color space conversion and color space conversion, which is applied in the field of color space conversion based on fuzzy theory and neural network, can solve problems such as global optimization of complex nonlinear functions that are not applicable, and achieve strong self-adaptive identification ability, improved accuracy, high precision effect

Active Publication Date: 2011-08-31
SHAANXI UNIV OF SCI & TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a color space conversion method based on fuzzy theory and neural network, which solves the problem that the existing color space conversion method is not suitable for solving the global optimization of complex nonlinear functions

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  • Color space transition method based on fuzzy logic and neural network
  • Color space transition method based on fuzzy logic and neural network
  • Color space transition method based on fuzzy logic and neural network

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Embodiment Construction

[0029] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] The present invention is based on the color space conversion method of fuzzy theory and neural network, specifically implements according to the following steps:

[0031] Step 1: Select the sampling point of the input color space, and collect the modeling data of the sampling point.

[0032] Check the color block: Divide the RGB color space into 27 subspaces (that is, 27 sub-cubes), select the center point of the cube as the verification point for verifying the accuracy of the model, a total of 27, and the values ​​​​of the R, G, and B color axes are 43, 129 and 213.

[0033] Modeling color blocks: select 729 sample points in the RGB color space. The selection of sampling points is to uniformly take points in the RGB color space, divide the R, G, and B color axes into eight equal parts, and the R, G, and B points of the training samples...

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Abstract

The invention discloses a color space transition method based on the fuzzy logic and the neural network, comprising steps of selecting sampling points of input color space, collecting modeling data of the sampling points, creating a fuzzy nerve color space transition model, and inputting the modeling data of the sampling points into the fuzzy nerve color space transition model to accomplish the transition of the color space. The color space transition method based on the fuzzy logic and the neural network of the invention makes full use of fuzzy identification on advantage of rapidity in WAN firstly, and the input color space is divided into a plurality of subspaces; then the invention also makes full use of neural network on advantages of strong ability and high precision on self-adaption and identification in local subspace and reasonably achieves the complementary advantages between fuzzy and neural network arithmetic, thereby greatly improving the processing speed of model conversion and effectively improving the precision of model conversion.

Description

technical field [0001] The invention belongs to the technical field of printing color management, and relates to a color space conversion method, in particular to a color space conversion method based on fuzzy theory and neural network. Background technique [0002] Color management technology is to solve the problem of correct color transfer between different imaging devices. One of the cores of color management technology is the mutual conversion of color space models. At present, the commonly used color space conversion methods mainly include Neusselberger equation method, three-dimensional look-up table (3D_LUT), polynomial regression method, and some artificial intelligence methods, such as artificial neural network, fuzzy logic and genetic algorithm. The chromaticity space transformation belongs to the nonlinear mapping problem of three-dimensional space, and the traditional mathematical model method has certain limitations, so it is difficult to be applied in the auto...

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Application Information

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IPC IPC(8): H04N1/60H04N9/67G06N3/02
Inventor 智川周世生石毅梁巧萍
Owner SHAANXI UNIV OF SCI & TECH
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