Affiliations: Intelligent Systems Laboratory, Halmstad University,
Box 823, S 301 18 Halmstad, Sweden | Department of Applied Electronics, Kaunas University
of Technology, 3031, Kaunas, Lithuania
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.
