Kubelka-Munk or neural networks for computer colorant formulation?

Stephen Westland; Laura Iovine; John M. Bishop

doi:10.1117/12.464656

6 June 2002 Kubelka-Munk or neural networks for computer colorant formulation?

Stephen Westland, Laura Iovine, John M. Bishop

Proceedings Volume 4421, 9th Congress of the International Colour Association; (2002) https://doi.org/10.1117/12.464656
Event: 9th Congress of the International Color Association, 2001, Rochester, NY, United States

Abstract

Traditionally Computer Colorant Formulation has been implemented using a theory of radiation transfer known as Kubelka-Munk (K-M) theory. Kubelka-Munk theory allows the prediction of spectral reflectance for a mixture of components (colorants) that have been characterised by absorption K and scattering S coefficients. More recently it has been suggested that Artifical Neural Networks ANNs) may be able to provide alternative mappings between colorant concentrations and spectral reflectances and, more generally, are able to provide transforms between color spaces. This study investigates the ability of ANNs to predict spectral reflectance from colorant concentrations using a set of data measured from known mixtures of lithographic printing inks. The issue of over-training is addressed and we show that the number of hidden units in the network must be carefully selected. We show that it is difficult to train a conventional neural network to the level that matches the performance that can be achieved using the K-M theory. However, a hybrid model is proposed that may out-perform the K-M model.

Citation Download Citation

Stephen Westland, Laura Iovine, and John M. Bishop "Kubelka-Munk or neural networks for computer colorant formulation?", Proc. SPIE 4421, 9th Congress of the International Colour Association, (6 June 2002); https://doi.org/10.1117/12.464656

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