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Conference Paper: A robust segmentation method for the AFCM-MRF model in noisy image
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TitleA robust segmentation method for the AFCM-MRF model in noisy image
 
AuthorsTam, SCF
Leung, CC
Tsui, WK
 
Issue Date2009
 
PublisherIEEE Press Piscataway.
 
CitationIeee International Conference On Fuzzy Systems, 2009, p. 379-383 [How to Cite?]
DOI: http://dx.doi.org/10.1109/FUZZY.2009.5277193
 
AbstractA robust image segmentation algorithm based on Alternative Fuzzy C-mean clustering algorithm (AFCM) with Markov Random Field (MRF) is presented in this paper. Due to disregard of spatial constraint information, the results using Fuzzy C-Mean (FCM) and AFCM are corrupted by noise. In order to improve the robustness of noise, the spatial constraint information of an image is represented by MRF with the Gibbs function which is based on the AFCM. Comparison to the FCM, AFCM, FCM-MRF model, and the proposed algorithm has been demonstrated by the simulation and real images. Results show that AFCM-MRF model achieves better performance than other methods. ©2009 IEEE.
 
ISSN1098-7584
2012 SCImago Journal Rankings: 0.237
 
DOIhttp://dx.doi.org/10.1109/FUZZY.2009.5277193
 
ISI Accession Number IDWOS:000274242600066
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorTam, SCF
 
dc.contributor.authorLeung, CC
 
dc.contributor.authorTsui, WK
 
dc.date.accessioned2010-09-25T18:30:12Z
 
dc.date.available2010-09-25T18:30:12Z
 
dc.date.issued2009
 
dc.description.abstractA robust image segmentation algorithm based on Alternative Fuzzy C-mean clustering algorithm (AFCM) with Markov Random Field (MRF) is presented in this paper. Due to disregard of spatial constraint information, the results using Fuzzy C-Mean (FCM) and AFCM are corrupted by noise. In order to improve the robustness of noise, the spatial constraint information of an image is represented by MRF with the Gibbs function which is based on the AFCM. Comparison to the FCM, AFCM, FCM-MRF model, and the proposed algorithm has been demonstrated by the simulation and real images. Results show that AFCM-MRF model achieves better performance than other methods. ©2009 IEEE.
 
dc.description.natureLink_to_subscribed_fulltext
 
dc.identifier.citationIeee International Conference On Fuzzy Systems, 2009, p. 379-383 [How to Cite?]
DOI: http://dx.doi.org/10.1109/FUZZY.2009.5277193
 
dc.identifier.doihttp://dx.doi.org/10.1109/FUZZY.2009.5277193
 
dc.identifier.epage383
 
dc.identifier.hkuros169565
 
dc.identifier.isiWOS:000274242600066
 
dc.identifier.issn1098-7584
2012 SCImago Journal Rankings: 0.237
 
dc.identifier.scopuseid_2-s2.0-71249116478
 
dc.identifier.spage379
 
dc.identifier.urihttp://hdl.handle.net/10722/99438
 
dc.languageeng
 
dc.publisherIEEE Press Piscataway.
 
dc.relation.ispartofIEEE International Conference on Fuzzy Systems
 
dc.relation.referencesReferences in Scopus
 
dc.titleA robust segmentation method for the AFCM-MRF model in noisy image
 
dc.typeConference_Paper
 
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