Application of Bayesian belief network in reliable analysis for video deinterlacing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeon, Gwanggil | - |
dc.contributor.author | Falcon, Rafael | - |
dc.contributor.author | Kim, Donghyung | - |
dc.contributor.author | Lee, Rokkyu | - |
dc.contributor.author | Jeong, Jechang | - |
dc.date.accessioned | 2022-10-07T10:39:29Z | - |
dc.date.available | 2022-10-07T10:39:29Z | - |
dc.date.created | 2022-08-26 | - |
dc.date.issued | 2008-02 | - |
dc.identifier.issn | 0098-3063 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172137 | - |
dc.description.abstract | In this paper, we illustrate that Bayesian networks (BNs), which are also known as belief networks, are well-suited for image processing. We provide case studies on video deinterlacing methods. The proposed efforts at modeling weight measuring process involved in weight assignment of conventional deinterlacing methods that are commonly used for industrial world. Using probabilistic BNs, the system determines the weights and interpolates the missing pixels robustly. The results of empirical trial show that the proposed system can deal successfully with several types of images containing motion or detail.(1). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Application of Bayesian belief network in reliable analysis for video deinterlacing | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jeong, Jechang | - |
dc.identifier.doi | 10.1109/TCE.2008.4470034 | - |
dc.identifier.scopusid | 2-s2.0-41649102642 | - |
dc.identifier.wosid | 000253499600019 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, v.54, no.1, pp.123 - 130 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS | - |
dc.citation.title | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS | - |
dc.citation.volume | 54 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 123 | - |
dc.citation.endPage | 130 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | Bayesian networks | - |
dc.subject.keywordPlus | Inference engines | - |
dc.subject.keywordPlus | Interpolation | - |
dc.subject.keywordPlus | Motion compensation | - |
dc.subject.keywordPlus | Pixels | - |
dc.subject.keywordPlus | Probability | - |
dc.subject.keywordAuthor | Bayesian networks | - |
dc.subject.keywordAuthor | deinterlacing | - |
dc.subject.keywordAuthor | expert systems | - |
dc.subject.keywordAuthor | reasoning under uncertainty modeling | - |
dc.subject.keywordAuthor | statistical inference | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/4470034 | - |
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