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Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Department of Applied Electronics, Kaunas University of Technology, Lithuania.
2001 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 10, no 2, p. 117-130Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2001. Vol. 10, no 2, p. 117-130
Keywords [en]
Neural network, Classification, Fuzzy sets, Evidence theory, Colour, Image processing, Decision fusion, Self-organising map
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-3543ISI: 000179475300004Scopus ID: 2-s2.0-0035567159OAI: oai:DiVA.org:hh-3543DiVA, id: diva2:286796
Available from: 2010-01-15 Created: 2009-12-01 Last updated: 2018-01-12Bibliographically approved

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Verikas, Antanas

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf