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Gesture Classification Using Hidden Markov Models and Viterbi Path Counting
Liu, Nianjun and Lovell, Brian C. (2003). Gesture Classification Using Hidden Markov Models and Viterbi Path Counting. In: Sun, C., Talbot, H., Ourselin, S. and Adriaansen, T., Proceedings of the Seventh Biennial Australian Pattern Recognition Society Conference. The Seventh Biennial Australian Pattern Recognition Society Conference, Sydney, (273-282). 10-12 December.
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| Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials) |
| Name |
Description |
MIMEType |
Size |
Downloads |
n0273.pdf
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n0273.pdf |
application/pdf |
385.16KB |
787 |
| Author(s) |
Liu, Nianjun
Lovell, Brian C.
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| Title of paper |
Gesture Classification Using Hidden Markov Models and Viterbi Path Counting
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| Conference name |
The Seventh Biennial Australian Pattern Recognition Society Conference
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| Conference location |
Sydney
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| Conference dates |
10-12 December
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| Proceedings title |
Proceedings of the Seventh Biennial Australian Pattern Recognition Society Conference
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| Editor(s) |
Sun, C.
Talbot, H.
Ourselin, S.
Adriaansen, T.
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| Place published |
Sydney
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| Publisher |
CSIRO
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| Publication date |
2003
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| Volume number |
1
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| ISBN |
1-74107-043-0
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| Start page |
273
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| End page |
282
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| Total pages |
10
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| Language |
eng
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| Abstract/Summary |
Human-Machine interfaces play a role of growing importance as computer technology continues to evolve. Motivated by the desire to provide users with an intuitive gesture input system, our work presented in this paper describes a Hidden Markov Model (HMM) based framework for hand gesture detection and recognition. The gesture is modeled as a Hidden Markov model. The observation sequence used to characterize the states of the HMM are obtained from the features extracted from the segmented hand image by Vector Quantization. In the recognition system, we try several different HMM models and training algorithms to find the algorithms with high recognition rate and low computational complexity.
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| Subjects |
280208 Computer Vision
280104 Computer-Human Interaction
280207 Pattern Recognition
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| Keyword(s) |
iris-research
computer vision
gesture
HMM
Markov
Hidden Markov Models
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