Difference between revisions of "Publications:Colour image segmentation by modular neural network"

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|Name=Verikas, Antanas [av] (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905], Halmstad Embedded and Intelligent Systems Research (EIS) [3938]);Malmqvist, Kerstin (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905]);Bergman, Lars (Högskolan i Halmstad [2804], Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) [3905])
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|Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Malmqvist, Kerstin (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905));Bergman, Lars (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905))
 
|Title=Colour image segmentation by modular neural network
 
|Title=Colour image segmentation by modular neural network
 
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|PublicationDate=2012-07-05
|LastUpdated=2013-06-11
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:539783}}
 
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Latest revision as of 21:41, 30 September 2016

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Title Colour image segmentation by modular neural network
Author
Year 1997
PublicationType Journal Paper
Journal Pattern Recognition Letters
HostPublication
Conference
DOI http://dx.doi.org/10.1016/S0167-8655(97)00004-4
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:539783
Abstract

In this paper segmentation of colour images is treated as a problem of classification of colour pixels. A hierarchical modular neural network for classification of colour pixels is presented. The network combines different learning techniques, performs analysis in a rough to fine fashion and enables to obtain a high average classification speed and a low classification error. Experimentally, we have shown that the network is capable of distinguishing among the nine colour classes that occur in an image. A correct classification rate of about 98% has been obtained even for two very similar black colours.