A Scanner Based Neural Network Technique for Color Matching of Dyed Cotton with Reactive Dye

Conventional theory for color matching is Kubelka-Munk, but it fails in some situations. New intelligent procedures such as neural networks could learn the behavior of a complex system and produce accurate prediction. This paper investigates the ability of MLP (multiple layer perceptron) neural network for color matching of cotton fabric. Three reactive dyes, namely Levafix Red CA, Levafix Yellow CA and Levafix Blue CA were used for experiments. The dyed samples were scanned and L*a*b* histogram were extracted. Different neural networks were trained and tested using L*a*b* histogram of fabric's images and also L*a*b* values (D65, 10 degrees) of fabrics. The results were encouraging. For neural networks including the L*a*b* histogram in input vector, colorants and their concentration were predicted with a mean square error (MSE) less than 10(-5) and an average value of color difference (CMC (1:2)) less than 1.5 for approximately 80 % of testing data.