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Cited 8 time in webofscience Cited 11 time in scopus
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dc.contributor.authorJaewon Sung-
dc.contributor.authorKim, D-
dc.date.accessioned2016-04-01T01:48:11Z-
dc.date.available2016-04-01T01:48:11Z-
dc.date.created2009-08-19-
dc.date.issued2007-01-
dc.identifier.issn0031-3203-
dc.identifier.other2006-OAK-0000006358-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/23734-
dc.description.abstractThis paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fitting algorithm consists of two alternating procedures: active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. We also suggest an effective fitness function for fitting the contour samples to the face boundary in the active contour technique; this function defines the quality of fitness in terms of the strength and/or the length of edge features. Experimental results show that the proposed active contour-based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate than the existing robust AAM. The combination of the existing robust AAM and the proposed active contour-based AAM (AC-R-AAM) had the best accuracy and convergence performances. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD-
dc.relation.isPartOfPATTERN RECOGNITION-
dc.subjectactive appearance model-
dc.subjectactive contour model-
dc.subjectrobust fitting algorithm-
dc.subjectmodel-based object tracking-
dc.subjectface tracking-
dc.titleA background robust active appearance model using active contour technique-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1016/j.patcog.2006.06.017-
dc.author.googleSung, J-
dc.author.googleKim, D-
dc.relation.volume40-
dc.relation.issue1-
dc.relation.startpage108-
dc.relation.lastpage120-
dc.contributor.id10054411-
dc.relation.journalPATTERN RECOGNITION-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationPATTERN RECOGNITION, v.40, no.1, pp.108 - 120-
dc.identifier.wosid000241837300009-
dc.date.tcdate2019-01-01-
dc.citation.endPage120-
dc.citation.number1-
dc.citation.startPage108-
dc.citation.titlePATTERN RECOGNITION-
dc.citation.volume40-
dc.contributor.affiliatedAuthorKim, D-
dc.identifier.scopusid2-s2.0-33749244820-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc8-
dc.type.docTypeArticle-
dc.subject.keywordAuthoractive appearance model-
dc.subject.keywordAuthoractive contour model-
dc.subject.keywordAuthorrobust fitting algorithm-
dc.subject.keywordAuthormodel-based object tracking-
dc.subject.keywordAuthorface tracking-
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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김대진KIM, DAI JIN
Dept of Computer Science & Enginrg
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