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Cited 13 time in webofscience Cited 16 time in scopus
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dc.contributor.authorPark, JS-
dc.contributor.authorHan, JH-
dc.date.accessioned2016-03-31T13:55:15Z-
dc.date.available2016-03-31T13:55:15Z-
dc.date.created2009-03-18-
dc.date.issued1998-03-16-
dc.identifier.issn0262-8856-
dc.identifier.other1998-OAK-0000000143-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/20830-
dc.description.abstractThe lack of information about tangential velocity makes velocity estimation erroneous in contour matching. Classical methods use the normal velocity, together with some smoothness constraints, since the tangential velocity cannot be recovered. This paper presents a contour matching method that computes displacements with a criteria of minimum curvature differences. The first derivative of tangential velocity is available from the image intensities and is related to the contour curvature. We compute the velocities using the curvature as well as the normal component. Consequently, the estimation error due to the tangential component is reduced substantially. A contour having occluding parts leads to mismatching. Our method determines occluding parts before the contour matching by analyzing the change of curvature distribution. Experimental results showed that the proposed method computes accurate velocity vectors for various moving contours. (C) 1998 Elsevier Science B.V.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.relation.isPartOfIMAGE AND VISION COMPUTING-
dc.subjectcontour matching-
dc.subjectcontour tracking-
dc.subjectcurvature-
dc.subjectvelocity field-
dc.subjectOPTICAL-FLOW-
dc.subjectIMAGE SEQUENCES-
dc.subjectMOTION-
dc.subjectMODELS-
dc.titleContour matching: a curvature-based approach-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1016/S0262-8856(97)00064-4-
dc.author.googlePark, JS-
dc.author.googleHan, JH-
dc.relation.volume16-
dc.relation.issue3-
dc.relation.startpage181-
dc.relation.lastpage189-
dc.contributor.id10077431-
dc.relation.journalIMAGE AND VISION COMPUTING-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationIMAGE AND VISION COMPUTING, v.16, no.3, pp.181 - 189-
dc.identifier.wosid000072791200003-
dc.date.tcdate2019-01-01-
dc.citation.endPage189-
dc.citation.number3-
dc.citation.startPage181-
dc.citation.titleIMAGE AND VISION COMPUTING-
dc.citation.volume16-
dc.contributor.affiliatedAuthorHan, JH-
dc.identifier.scopusid2-s2.0-0032024717-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc11-
dc.type.docTypeArticle-
dc.subject.keywordPlusOPTICAL-FLOW-
dc.subject.keywordPlusIMAGE SEQUENCES-
dc.subject.keywordPlusMOTION-
dc.subject.keywordPlusMODELS-
dc.subject.keywordAuthorcontour matching-
dc.subject.keywordAuthorcontour tracking-
dc.subject.keywordAuthorcurvature-
dc.subject.keywordAuthorvelocity field-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Software Engineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryOptics-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaOptics-

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한준희HAN, JOON HEE
Dept of Computer Science & Enginrg
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