Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11180
Title: Colour-appearance modeling using feedforward networks with Bayesian regularization method. Part II : reverse model
Authors: Xin, JH 
Shao, S
Chung, K 
Keywords: Back-propagation
Bayesian regularization
Colour appearance models
Feedforward neural networks
Issue Date: 2002
Publisher: John Wiley & Sons
Source: Color research & applications, 2002, v. 27, no. 2, p. 116-121 How to cite?
Journal: Color research & applications 
Abstract: In Part I of this article, the development of a multilayer perceptrons feedforward artificial neural network model to predict colour appearance from colorimetric values was reported. Bayesian regularization was employed for the training of the network. In this part of the article, the reverse model, that is, the perdition of colorimetric values from the colour appearance attributes is reported using the same neural network design methodology developed in Part I. This study should contribute to the building of an artificial neural network¡Vbased colour appearance prediction, both forward and reverse, using the most comprehensive LUTCHI colour appearance data sets for training and testing. Good prediction accuracy and generalization ability were obtained using the neural networks built in the study. Because the neural network approach is of a black-box type, colour appearance prediction using this method should be easier to apply in practice.
URI: http://hdl.handle.net/10397/11180
ISSN: 0361-2317
DOI: 10.1002/col.10030
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