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Article type: Research Article
Authors: Verikas, A.; | Malmqvist, K. | Bacauskiene, M.
Affiliations: Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden | Department of Applied Electronics, Kaunas University of Technology, 3031, Kaunas, Lithuania
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Abstract: An approach to detecting colour specks in an image taken from a pulp sample of recycled paper is presented. The task is solved through pixel-wise colour classification by an artificial neural network and post-processing based on the evidence theory. The network is trained using possibilistic target values, which are determined through a self-organising process in a 2D and 1D map of chromaticity and lightness, respectively. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.
Keywords: neural network, classification, fuzzy sets, evidence theory, colour image processing, decision fusion, self-organising map
Journal: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 2, pp. 117-130, 2001
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