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

<div style='display: none'>
== Do not edit this section ==
</div>
{{PublicationSetupTemplate|Author=Antanas Verikas, Kerstin Malmqvist, Marija Bacauskiene
|PID=541024
|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]) (Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania);Malmqvist, Kerstin [kermalx] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938], Intelligenta system (IS-lab) [3941]);Bacauskiene, Marija (Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania)
|Title=Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images
|PublicationType=Conference Paper
|ContentType=Refereegranskat
|Language=eng
|Journal=
|JournalISSN=
|Status=
|Volume=
|Issue=
|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
|StartPage=297
|EndPage=302
|Year=2000
|Edition=
|Pages=
|City=Los Alamitos
|Publisher=IEEE Computer Society
|Series=IEEE International Joint Conference on Neural Networks (IJCNN)
|SeriesISSN=1098-7576
|ISBN=9780769506197;0769506194;0780365410;9780780365414;0769506216;9780769506210
|Urls=
|ISRN=
|DOI=http://dx.doi.org/10.1109/IJCNN.2000.857912
|ISI=000089240200047
|PMID=
|ScopusId=2-s2.0-0033681952
|NBN=urn:nbn:se:hh:diva-18783
|LocalId=
|ArchiveNumber=
|Keywords=
|Categories=Teknik och teknologier (2)
|ResearchSubjects=
|Projects=
|Notes=
|Abstract=<p>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.</p>
|Opponents=
|Supervisors=
|Examiners=
|Patent=
|ThesisLevel=
|Credits=
|Programme=
|Subject=
|Uppsok=
|DefencePlace=
|DefenceLanguage=
|DefenceDate=
|CreatedDate=2012-06-25
|PublicationDate=2012-07-13
|LastUpdated=2014-02-26
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:541024}}
<div style='display: none'>

== Keep all hand-made modifications below ==
</div>{{PublicationDisplayTemplate}}