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dc.citation.endPage 1270 -
dc.citation.number 5 -
dc.citation.startPage 1265 -
dc.citation.title IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS -
dc.citation.volume E87D -
dc.contributor.author Kim, JB -
dc.contributor.author Bien, Zeungnam -
dc.date.accessioned 2023-12-22T11:06:24Z -
dc.date.available 2023-12-22T11:06:24Z -
dc.date.created 2014-11-24 -
dc.date.issued 2004-05 -
dc.description.abstract We 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.identifier.bibliographicCitation IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E87D, no.5, pp.1265 - 1270 -
dc.identifier.issn 0916-8532 -
dc.identifier.scopusid 2-s2.0-2642571855 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/9222 -
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=2642571855 -
dc.identifier.wosid 000221445100027 -
dc.language 영어 -
dc.publisher IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG -
dc.title Recognition of continuous Korean sign language using gesture tension model and soft computing technique -
dc.type Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -

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