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Article: On modelling, extraction, detection and classification of deformable contours from noisy images

TitleOn modelling, extraction, detection and classification of deformable contours from noisy images
Authors
KeywordsComputer vision
Deformable model
Image processing
Pattern recognition
Issue Date1998
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavis
Citation
Image And Vision Computing, 1998, v. 16 n. 1, p. 55-62 How to Cite?
AbstractWe present an integrated approach in modelling, extracting, detecting and classifying deformable contours directly from noisy images, based on the generalized active contour models (g-snakes) [1]. Our contour representation for an arbitrary shape is stable and regenerative, as well as invariant and unique under affine motions. We combine this shape model with Markov random fields to yield prior distribution that exerts influence over the arbitrary shape while allowing for deformation. Using our formulation, low level visual tasks of shape modelling and extraction can be readily integrated with high level detection and classification. © 1998 Elsevier Science B.V.
Persistent Identifierhttp://hdl.handle.net/10722/65533
ISSN
2015 Impact Factor: 1.766
2015 SCImago Journal Rankings: 1.700
References

 

DC FieldValueLanguage
dc.contributor.authorLai, KFen_HK
dc.contributor.authorChin, RTen_HK
dc.date.accessioned2010-08-31T07:15:10Z-
dc.date.available2010-08-31T07:15:10Z-
dc.date.issued1998en_HK
dc.identifier.citationImage And Vision Computing, 1998, v. 16 n. 1, p. 55-62en_HK
dc.identifier.issn0262-8856en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65533-
dc.description.abstractWe present an integrated approach in modelling, extracting, detecting and classifying deformable contours directly from noisy images, based on the generalized active contour models (g-snakes) [1]. Our contour representation for an arbitrary shape is stable and regenerative, as well as invariant and unique under affine motions. We combine this shape model with Markov random fields to yield prior distribution that exerts influence over the arbitrary shape while allowing for deformation. Using our formulation, low level visual tasks of shape modelling and extraction can be readily integrated with high level detection and classification. © 1998 Elsevier Science B.V.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavisen_HK
dc.relation.ispartofImage and Vision Computingen_HK
dc.subjectComputer visionen_HK
dc.subjectDeformable modelen_HK
dc.subjectImage processingen_HK
dc.subjectPattern recognitionen_HK
dc.titleOn modelling, extraction, detection and classification of deformable contours from noisy imagesen_HK
dc.typeArticleen_HK
dc.identifier.emailChin, RT: rchin@hku.hken_HK
dc.identifier.authorityChin, RT=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0031680973en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031680973&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume16en_HK
dc.identifier.issue1en_HK
dc.identifier.spage55en_HK
dc.identifier.epage62en_HK
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLai, KF=7402134987en_HK
dc.identifier.scopusauthoridChin, RT=7102445426en_HK

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