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Conference Paper: A robust segmentation method for the AFCM-MRF model in noisy image

TitleA robust segmentation method for the AFCM-MRF model in noisy image
Authors
Tam, SCFLeung, CCTsui, WK
Issue Date2009
PublisherIEEE Press Piscataway.
Citation
Ieee 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.
Persistent Identifierhttp://hdl.handle.net/10722/99438
ISSN
1098-7584
2020 SCImago Journal Rankings: 0.280
ISI Accession Number ID
WOS:000274242600066
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorTam, SCFen_HK
dc.contributor.authorLeung, CCen_HK
dc.contributor.authorTsui, WKen_HK
dc.date.accessioned2010-09-25T18:30:12Z-
dc.date.available2010-09-25T18:30:12Z-
dc.date.issued2009en_HK
dc.identifier.citationIeee International Conference On Fuzzy Systems, 2009, p. 379-383en_HK
dc.identifier.issn1098-7584en_HK
dc.identifier.urihttp://hdl.handle.net/10722/99438-
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.en_HK
dc.languageengen_HK
dc.publisherIEEE Press Piscataway.en_HK
dc.relation.ispartofIEEE International Conference on Fuzzy Systemsen_HK
dc.titleA robust segmentation method for the AFCM-MRF model in noisy imageen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailTsui, WK:wktsui@eee.hku.hken_HK
dc.identifier.authorityTsui, WK=rp00182en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/FUZZY.2009.5277193en_HK
dc.identifier.scopuseid_2-s2.0-71249116478en_HK
dc.identifier.hkuros169565en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-71249116478&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage379en_HK
dc.identifier.epage383en_HK
dc.identifier.isiWOS:000274242600066-
dc.identifier.scopusauthoridTam, SCF=35226619100en_HK
dc.identifier.scopusauthoridLeung, CC=36725507200en_HK
dc.identifier.scopusauthoridTsui, WK=7005623168en_HK
dc.identifier.issnl1098-7584-

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