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Conference Paper: Multiclass segmentation based on generalized fuzzy Gibbs random fields

TitleMulticlass segmentation based on generalized fuzzy Gibbs random fields
Authors
KeywordsMult-class segmentation
generalized fiyzy GRFs
image segmentation
Issue Date2003
PublisherIEEE.
Citation
International Conference on Image Processing Proceedings, Barcelona, Spain, 14-17 September 2003, v. 2, p. 399-402 How to Cite?
AbstractThe model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered. In this paper, two multiclass approaches, based on the model of piecewise fuzzy Gibbs random fields (PFGRF) and that of generalized fuzzy Gibbs random fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical 'hard' one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.
Persistent Identifierhttp://hdl.handle.net/10722/46508
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLin, YZen_HK
dc.contributor.authorChen, WFen_HK
dc.contributor.authorChan, FHYen_HK
dc.date.accessioned2007-10-30T06:51:33Z-
dc.date.available2007-10-30T06:51:33Z-
dc.date.issued2003en_HK
dc.identifier.citationInternational Conference on Image Processing Proceedings, Barcelona, Spain, 14-17 September 2003, v. 2, p. 399-402en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46508-
dc.description.abstractThe model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered. In this paper, two multiclass approaches, based on the model of piecewise fuzzy Gibbs random fields (PFGRF) and that of generalized fuzzy Gibbs random fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical 'hard' one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.en_HK
dc.format.extent358201 bytes-
dc.format.extent13817 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectMult-class segmentationen_HK
dc.subjectgeneralized fiyzy GRFsen_HK
dc.subjectimage segmentationen_HK
dc.titleMulticlass segmentation based on generalized fuzzy Gibbs random fieldsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1522-4880&volume=2&spage=399&epage=402&date=2003&atitle=Multiclass+segmentation+based+on+generalized+fuzzy+Gibbs+random+fieldsen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICIP.2003.1246701en_HK
dc.identifier.hkuros95134-

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