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Publication
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| Title of Article |
Automatic Hand Sign Recognition: Identify Unusuality Through Latent Cognizance |
| Date of Acceptance |
7 July 2018 |
| Journal |
| Title of Journal |
Lecture Notes in Computer Science (LNCS) |
| Standard |
SCOPUS |
| Institute of Journal |
springer |
| ISBN/ISSN |
978-3-319-99977-7 |
| Volume |
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| Issue |
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| Month |
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| Year of Publication |
2018 |
| Page |
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| Abstract |
Sign language is a main communication channel among a hearing disability community. Automatic sign language transcription could facilitate better communication and understanding between a hearing disability community and a hearing majority.
As a recent work in automatic sign language transcription has discussed, effectively handling or identifying a non-sign posture is one of the key issues. A non-sign posture is a posture unintended for sign reading and does not belong to any valid sign. A non-sign posture may arise during a sign transition or simply from an unaware posture. Confidence ratio (CR) has been proposed to mitigate the issue. CR is simple to compute and readily available without extra training. However, CR is reported to only partially address the problem. In addition, CR formulation is susceptible to computational instability.
This article proposes alternative formulations to CR, investigates an issue of non-sign identification for Thai Finger Spelling recognition, explores potential solutions and has found a promising direction. Not only does this finding address the issue of non-sign identification, it also provide an insight behind a well-learned inference machine, revealing hidden meaning and new interpretation of the underlying mechanism. Our proposed methods are evaluated and shown to be effective for non-sign detection. |
| Keyword |
Hand sign recognition,Thai Finger Spelling, Open-set detection, Novelty detection, Zero-shot learning, Inference interpretation |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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