Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Rocco Furferi; Lapo Governi; Yary Volpe

doi:10.1117/1.JEI.25.6.061402

21 April 2016 Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

Rocco Furferi, Lapo Governi, Yary Volpe

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 6, 061402 (April 2016). https://doi.org/10.1117/1.JEI.25.6.061402

Abstract

Color matching of fabric blends is a key issue for the textile industry, mainly due to the rising need to create high-quality products for the fashion market. The process of mixing together differently colored fibers to match a desired color is usually performed by using some historical recipes, skillfully managed by company colorists. More often than desired, the first attempt in creating a blend is not satisfactory, thus requiring the experts to spend efforts in changing the recipe with a trial-and-error process. To confront this issue, a number of computer-based methods have been proposed in the last decades, roughly classified into theoretical and artificial neural network (ANN)–based approaches. Inspired by the above literature, the present paper provides a method for accurate estimation of spectrophotometric response of a textile blend composed of differently colored fibers made of different materials. In particular, the performance of the Kubelka-Munk (K-M) theory is enhanced by introducing an artificial intelligence approach to determine a more consistent value of the nonlinear function relationship between the blend and its components. Therefore, a hybrid K-M+ANN-based method capable of modeling the color mixing mechanism is devised to predict the reflectance values of a blend.

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Rocco Furferi, Lapo Governi, and Yary Volpe "Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method," Journal of Electronic Imaging 25(6), 061402 (21 April 2016). https://doi.org/10.1117/1.JEI.25.6.061402

Published: 21 April 2016

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