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Cited 14 time in webofscience Cited 15 time in scopus
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dc.contributor.authorPark, JS-
dc.contributor.authorHan, JH-
dc.date.accessioned2016-03-31T13:57:26Z-
dc.date.available2016-03-31T13:57:26Z-
dc.date.created2009-03-18-
dc.date.issued1998-01-
dc.identifier.issn0031-3203-
dc.identifier.other1998-OAK-0000000048-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/20898-
dc.description.abstractThis paper presents a novel method of velocity field estimation for the points on moving contours in a 2-D image sequence. The method determines the corresponding point in a next image frame by considering the curvature change of a given point on the contour. In traditional methods, there are errors in optical flow estimation for the points which have low curvature variations since those methods compute solutions by approximating normal optical flow. The proposed method computes optical flow vectors of contour points minimizing the curvature changes. As a first step, snakes are used to locate smooth curves in 2-D imagery. Thereafter, the extracted curves are tracked continuously. Each point on a contour has a unique corresponding point on the contour in the next frame whenever the curvature distribution of the contour varies smoothly. The experimental results showed that the proposed method computes accurate optical flow vectors for various moving contours. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.relation.isPartOfPATTERN RECOGNITION-
dc.subjectcontour motion-
dc.subjectoptical flow-
dc.subjectsnakes-
dc.subjecttracking-
dc.subjectcontour matching-
dc.subjectOPTICAL-FLOW-
dc.subjectCORNER DETECTION-
dc.subjectTRACKING-
dc.titleContour motion estimation from image sequences using curvature information-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1016/s0031-3203(97)00031-9-
dc.author.googlePark, JS-
dc.author.googleHan, JH-
dc.relation.volume31-
dc.relation.issue1-
dc.relation.startpage31-
dc.relation.lastpage39-
dc.contributor.id10077431-
dc.relation.journalPATTERN RECOGNITION-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationPATTERN RECOGNITION, v.31, no.1, pp.31 - 39-
dc.identifier.wosid000071506300004-
dc.date.tcdate2019-01-01-
dc.citation.endPage39-
dc.citation.number1-
dc.citation.startPage31-
dc.citation.titlePATTERN RECOGNITION-
dc.citation.volume31-
dc.contributor.affiliatedAuthorHan, JH-
dc.identifier.scopusid2-s2.0-0031633646-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc14-
dc.type.docTypeArticle-
dc.subject.keywordPlusOPTICAL-FLOW-
dc.subject.keywordPlusCORNER DETECTION-
dc.subject.keywordPlusTRACKING-
dc.subject.keywordAuthorcontour motion-
dc.subject.keywordAuthoroptical flow-
dc.subject.keywordAuthorsnakes-
dc.subject.keywordAuthortracking-
dc.subject.keywordAuthorcontour matching-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-

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한준희HAN, JOON HEE
Dept of Computer Science & Enginrg
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