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6 April 1995 Cooperative system based on soft computing methods to realize higher precision of computer color recipe prediction
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eiji Mizutani, Hideyuki Takagi, and David M. Auslander "Cooperative system based on soft computing methods to realize higher precision of computer color recipe prediction", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995);


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