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Proceedings Paper

Cooperative system based on soft computing methods to realize higher precision of computer color recipe prediction
Author(s): Eiji Mizutani; Hideyuki Takagi; David M. Auslander
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Paper Abstract

This paper proposes a combinational model of neural networks (NNs) and a genetic algorithm (GA) to obtain highly precise outputs. Performance of the model is evaluated by application to a computerized color recipe prediction task, which requires relating surface spectral reflectance of a target color to several pigment concentrations. For GA search, first, predictive concentrations of color pigments are initialized by a random initializer, a multi-elite generator based on rules, and an NN which predicts pigment concentrations from the surface spectral reflectance. Then the GA starts searching for more precise pigment concentration vectors depending on a fitness function which is constructed based on three functions: (1) an NN to predict which pigments to use, (2) a rule base to deal with knowledge of color, and (3) an NN to calculate color difference to take into account human visual sensitivity. This hybrid model predicts color pigment concentrations with higher precision by fine-tuning the results of NN approaches. It may possibly show great potential in another precision problem.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205175
Show Author Affiliations
Eiji Mizutani, Kansai Paint Co., Ltd. (Japan)
Hideyuki Takagi, Matsushita Electric Industrial Co., Ltd. (Japan)
David M. Auslander, Univ. of California/Berkeley (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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