The recycling of wool clothes: an artificial neural network colour classification tool

Recycling of clothes is a straightforward approach for the supply of a coloured raw material which does not involve the cost of the colouring process. A real time and completely automated colour classification tool for woollen clothes to be recycled is proposed. The tool uses the combination of a statistical method, called matrix approach, of a self-organizing feature map (SOFM) and a feed-forward backpropagation artificial neural network (FFBP ANN)-based approach, to correctly classify the clothes by respecting the selection criteria provided by human know-how. The developed tool, which uses an appositely developed workbench with a spectrophotometer, is aware of the way the different coloured clothes to be recycled combine each other to create a new one. The tool has been validated using a set of 5,000 differently coloured clothes to be recycled and the classification error in classifying the clothes is within 5%, i.e., lower than the one resulting from the use of an expert human operator.

[1]  Stephen R. Marsland,et al.  A self-organising network that grows when required , 2002, Neural Networks.

[2]  Brian V. Funt,et al.  Colour by Correlation in a Three-Dimensional Colour Space , 2000, ECCV.

[3]  Denis Martin,et al.  Improved model for improving the inter-instrument agreement of spectrocolorimeters , 1999 .

[4]  María S. Millán,et al.  Adaptive channel selection for improving chromatic discrimination in colour pattern recognition , 1997 .

[5]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[6]  Ming Ronnier Luo,et al.  Applying colour science in colour design , 2006 .

[7]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[8]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[9]  Stavros Papadokonstantakis,et al.  Comparison of recent methods for inference of variable influence in neural networks , 2006, Neural Networks.

[10]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[11]  George K. Knopf,et al.  Visualization of randomly ordered numeric data sets using spherical self-organizing feature maps , 2003, Comput. Graph..

[12]  Cheryl Pellerin.  Machine vision in experimental poultry inspection , 1995 .

[13]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[14]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..