Real-Time Hand Gesture Recognition using Pseudo 3-D Hidden Markov Model
In the following work we present a new approach to recognition of hand gesture based on pseudo three-dimensional Hidden Markov Model (P3DHMM), a technique which can integrate spatial as well as temporal derived features in an elegant and efficient way. Additionally, robust and flexible hand gesture tracking using an appearance-based condensation tracker. These allow the recognition of dynamic gestures as well as more static gestures. Furthermore, there has been proposed to improve the overall performance of the approach: Replace Bourn-Welch algorithm with clustering algorithm, adding a clustering performance measure to the clustering algorithm and adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. Proposed improving methods along with the P3DHMM was used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a recognition rate of 99.1％ in the gesture recognition experiments when compared to P2DHMMs.
Pseudo 3-D Hidden Markov Model hand gesture recognition condensation algorithm.
Nguyen Dang Binh Toshiaki Ejima
Intelligence Media Laboratory, Kyushu Institute of Technology 820-4, Kawazu, lizuka, Fukuoka 820, Japan
国际会议
Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)
北京
英文
820-824
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）