dc.citation.endPage |
1270 |
- |
dc.citation.number |
5 |
- |
dc.citation.startPage |
1265 |
- |
dc.citation.title |
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
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dc.citation.volume |
E87D |
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dc.contributor.author |
Kim, JB |
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dc.contributor.author |
Bien, Zeungnam |
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dc.date.accessioned |
2023-12-22T11:06:24Z |
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dc.date.available |
2023-12-22T11:06:24Z |
- |
dc.date.created |
2014-11-24 |
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dc.date.issued |
2004-05 |
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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 |
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dc.identifier.bibliographicCitation |
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E87D, no.5, pp.1265 - 1270 |
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dc.identifier.issn |
0916-8532 |
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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 |
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dc.identifier.wosid |
000221445100027 |
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dc.language |
영어 |
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dc.publisher |
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG |
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dc.title |
Recognition of continuous Korean sign language using gesture tension model and soft computing technique |
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dc.type |
Article |
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dc.description.journalRegisteredClass |
scie |
- |
dc.description.journalRegisteredClass |
scopus |
- |