Publications:Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images

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Title Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images
Author Antanas Verikas and Kerstin Malmqvist and Marija Bacauskiene
Year 2000
PublicationType Conference Paper
Journal
HostPublication IJCNN 2000 : Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, 24-27 July 2000, Vol. 2
Conference International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, July 24-27, 2000
DOI http://dx.doi.org/10.1109/IJCNN.2000.857912
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:541024
Abstract This paper presents an approach to determining colours of specks in an image taken from a pulp sample. The task is solved through colour classification by an artificial neural network. The network is trained using possibilistic target values. 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 experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.