Gesture spotting from continuous hand motion

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This paper proposes a new method of gesture spotting based on the Hidden Markov Model (HMM) that extracts meaningful gestures from continuous hand motion. To remove non-gesture patterns from input patterns, we introduce the threshold model that calculates the threshold likelihood of the input pattern and helps to qualify an input pattern as a gesture. The proposed method extracts gestures with 93.38% reliability. (C) 1998 Elsevier Science B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
1998-04
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION LETTERS, v.19, no.5-6, pp.513 - 520

ISSN
0167-8655
URI
http://hdl.handle.net/10203/13886
Appears in Collection
CS-Journal Papers(저널논문)
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