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dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorYan, Hongen_US
dc.contributor.authorLaw, N.en_US
dc.date.accessioned2017-04-24T12:52:21Z
dc.date.available2017-04-24T12:52:21Z
dc.date.issued2005en_US
dc.date.modified2009-03-26T06:42:29Z
dc.identifier.issn1063-6706en_US
dc.identifier.doi10.1109/TFUZZ.2004.841748en_AU
dc.identifier.urihttp://hdl.handle.net/10072/21801
dc.description.abstractAn image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent1439231 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=91en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom444en_US
dc.relation.ispartofpageto453en_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalIEEE Transactions on Fuzzy Systemsen_US
dc.relation.ispartofvolume13en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280203en_US
dc.titleImage Segmentation Based on Adaptive Cluster Prototype Estimationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.rights.copyrightCopyright 2005 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_AU
gro.date.issued2005
gro.hasfulltextFull Text


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