Special Section on Color in Texture and Material Recognition

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

[+] Author Affiliations
Rocco Furferi, Lapo Governi, Yary Volpe

University of Florence, Department of Industrial Engineering, Via di Santa Marta, 3 Firenze, 50139, Italy

J. Electron. Imaging. 25(6), 061402 (Apr 21, 2016). doi:10.1117/1.JEI.25.6.061402
History: Received November 6, 2015; Accepted January 12, 2016
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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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Citation

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


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