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Publications:Colour image segmentation by modular neural network
Abstract <p>In this paper segmentation of col<p>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.</p> two very similar black colours.</p>
Author Antanas Verikas + , Kerstin Malmqvist + , Lars Bergman +
DOI http://dx.doi.org/10.1016/S0167-8655(97)00004-4  +
Diva http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:539783
EndPage 185  +
Issue 2  +
Journal Pattern Recognition Letters  +
PublicationType Journal Paper  +
Publisher Elsevier  +
StartPage 173  +
Title Colour image segmentation by modular neural network  +
Volume 18  +
Year 1997  +
Has queryThis property is a special property in this wiki. Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network + , Publications:Colour image segmentation by modular neural network +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 30 September 2016 20:41:11  +
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