Recognition of continuous Korean sign language using gesture tension model and soft computing technique

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DC FieldValueLanguage
dc.contributor.authorKim, JBko
dc.contributor.authorBien, ZNko
dc.date.accessioned2013-03-03T20:20:10Z-
dc.date.available2013-03-03T20:20:10Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2004-05-
dc.identifier.citationIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E87D, pp.1265 - 1270-
dc.identifier.issn0916-8532-
dc.identifier.urihttp://hdl.handle.net/10203/80274-
dc.description.abstractWe present a method for recognition of continuous Korean Sign Language (KSL). In the paper, we consider the segmentation problem of a continuous hand motion pattern in KSL. For this, we first extract sign sentences by removing linking gestures between sign sentences. We use a gesture tension model and fuzzy partitioning. Then, each sign sentence is disassembled into a set of elementary motions (EMs) according to its geometric pattern. The hidden Markov model is adopted to classify the segmented individual EMs.-
dc.languageEnglish-
dc.publisherIEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG-
dc.titleRecognition of continuous Korean sign language using gesture tension model and soft computing technique-
dc.typeArticle-
dc.identifier.wosid000221445100027-
dc.identifier.scopusid2-s2.0-2642571855-
dc.type.rimsART-
dc.citation.volumeE87D-
dc.citation.beginningpage1265-
dc.citation.endingpage1270-
dc.citation.publicationnameIEICE TRANSACTIONS ON INFORMATION AND SYSTEMS-
dc.contributor.localauthorBien, ZN-
dc.contributor.nonIdAuthorKim, JB-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorsign language recognition-
dc.subject.keywordAuthorgesture recognition-
dc.subject.keywordAuthorcontinuous gesture-
dc.subject.keywordAuthorgesture tension model-
dc.subject.keywordAuthorsoft computing-
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