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Designing rough sets attributes reduction based video deinterlacing system

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dc.contributor.authorJeon, Gwanggil-
dc.contributor.authorAnisetti, Marco-
dc.contributor.authorBellandi, Valerio-
dc.contributor.authorJeong, Jechang-
dc.date.accessioned2022-12-21T08:40:57Z-
dc.date.available2022-12-21T08:40:57Z-
dc.date.created2022-09-16-
dc.date.issued2007-04-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180238-
dc.description.abstractThe use of rough set's theoretic concepts has permitted in this work to make the mathematical model on mode decision in deinterlacing system. In this paper, a rough set approach based decision making problem is proposed. In the literature, some conventional deinterlacing methods provide high performance with higher computational burden. On the other hand, some other methods give low performance with lower computational burden. Those all methods have been reported that interpolate missing pixels indiscriminately in the same way. Our algorithm chooses the most suitable method adaptively based on rough set theory using four parameters. This deinterlacing approach employs a size reduction of the database system, keeping only the essential information for the process, especially in the representation of and reasoning with vague and/or imprecise knowledge. Decision making and interpolation results are presented. The results of computer simulations show that the proposed method outperforms a number of methods presented in the literature.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleDesigning rough sets attributes reduction based video deinterlacing system-
dc.typeArticle-
dc.contributor.affiliatedAuthorJeong, Jechang-
dc.identifier.doi10.1007/978-3-540-71618-1_76-
dc.identifier.scopusid2-s2.0-38049021970-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4431 LNCS, no.PART 1, pp.684 - 693-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume4431 LNCS-
dc.citation.numberPART 1-
dc.citation.startPage684-
dc.citation.endPage693-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusDecision making-
dc.subject.keywordPlusInterpolation-
dc.subject.keywordPlusMathematical models-
dc.subject.keywordPlusPixels-
dc.subject.keywordPlusProblem solving-
dc.subject.keywordPlusDecision making problem-
dc.subject.keywordPlusMode decision-
dc.subject.keywordPlusVideo deinterlacing system-
dc.subject.keywordPlusRough set theory-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-540-71618-1_76-
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