Publications:Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks

From ISLAB/CAISR

Do not edit this section

Keep all hand-made modifications below

Title Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks
Author Antanas Verikas and Kerstin Malmqvist and Marija Bacauskiene
Year 2001
PublicationType Journal Paper
Journal Journal of Intelligent & Fuzzy Systems
HostPublication
Conference
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:286796
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.