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Conference Paper: Deformable contours: modeling and extraction

TitleDeformable contours: modeling and extraction
Authors
KeywordsDeformation
Estimation
Image processing
Mathematical models
Mathematical transformations
Matrix algebra
Optimization
Performance
Random processes
Issue Date1994
PublisherIEEE, Computer Society
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, p. 601-608 How to Cite?
AbstractThis paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.
Persistent Identifierhttp://hdl.handle.net/10722/65571
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLai, Kok Fen_HK
dc.contributor.authorChin, Roland Ten_HK
dc.date.accessioned2010-08-31T07:16:11Z-
dc.date.available2010-08-31T07:16:11Z-
dc.date.issued1994en_HK
dc.identifier.citationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994, p. 601-608en_HK
dc.identifier.issn1063-6919en_HK
dc.identifier.urihttp://hdl.handle.net/10722/65571-
dc.description.abstractThis paper considers the problem of modeling and extracting arbitrary deformable contours from noisy images. We propose a global contour model based on a stable and regenerative shape matrix, which is invariant and unique under rigid motions. Combined with Markov random field to model local deformations, this yields prior distribution that exerts influence over a global model while allowing for deformations. We then cast the problem of extraction into posterior estimation and show its equivalence to energy minimization of a generalized active contour model. We discuss pertinent issues in shape training, minimax regularization and initialization by generalized Hough transform. Finally, we present experimental results and compare its performance to rigid template matching.en_HK
dc.languageengen_HK
dc.publisherIEEE, Computer Societyen_HK
dc.relation.ispartofProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionen_HK
dc.subjectDeformationen_HK
dc.subjectEstimationen_HK
dc.subjectImage processingen_HK
dc.subjectMathematical modelsen_HK
dc.subjectMathematical transformationsen_HK
dc.subjectMatrix algebraen_HK
dc.subjectOptimizationen_HK
dc.subjectPerformanceen_HK
dc.subjectRandom processesen_HK
dc.titleDeformable contours: modeling and extractionen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailChin, Roland T: rchin@hku.hken_HK
dc.identifier.authorityChin, Roland T=rp01300en_HK
dc.description.naturelink_to_subscribed_fulltexten_HK
dc.identifier.scopuseid_2-s2.0-0028087339en_HK
dc.identifier.spage601en_HK
dc.identifier.epage608en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLai, Kok F=7402134987en_HK
dc.identifier.scopusauthoridChin, Roland T=7102445426en_HK

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