Gesture spotting from continuous hand motion

Cited 10 time in webofscience Cited 0 time in scopus
  • Hit : 413
  • Download : 2
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)
Files in This Item
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 10 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0