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A Generic Fuzzy Rule Based Image Segmentation Algorithm

Karmakar, G. C. and Dooley, L. S. (2002). A Generic Fuzzy Rule Based Image Segmentation Algorithm. Pattern Recognition Letters, 23(10) pp. 1215–1227.

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Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, the corresponding parameters being manually determined. The net result is that the overall performance of the segmentation technique is very sensitive to parameter value selections. This paper addresses these issues by introducing a generic fuzzy rule based image segmentation (GFRIS) algorithm, which is both application independent and exploits inter-pixel spatial relationships. The GFRIS algorithm automatically approximates both the key weighting factor and threshold value in the definitions of the fuzzy rule and neighbourhood system, respectively. A quantitative evaluation is presented between the segmentation results obtained using GFRIS and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. The results demonstrate that GFRIS exhibits a considerable improvement in performance compared to both FCM and PCM, for many different image types.

Item Type: Journal Item
ISSN: 0167-8655
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 11400
Depositing User: Laurence Dooley
Date Deposited: 19 Aug 2008 06:37
Last Modified: 07 Dec 2018 09:11
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