Recognition of Arabic sign language alphabet using polynomial classifiers
EURASIP Journal on Applied Signal Processing
International Journal of Bioinformatics Research and Applications
An Approach to Glove-Based Gesture Recognition
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Comparison of human and machine recognition of everyday human actions
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
A cognitive vision system for action recognition in office environments
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Subject-independent natural action recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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This paper proposes an approach to 2D gesture recognition that models each gesture as a Finite State Machine (FSM) in the spatial-temporal space. The model construction works in a semi-automatic way. The structure of the model is first manually decided based on the observation of the spatial topology of the data. The model is refined iteratively between two stages: data segmentation and model training. We incorporate a modified Knuth-Morris-Pratt algorithm into FSM recognition procedure to speed up the gesture recognition. The computational efficiency of the FSM recognizers allows real-time on-line performance to be achieved.