Constructing finite state machines for fast gesture recognition

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 recognition procedure to speed up recognition. The computational efficiency of the FSM recognizers allows real-time online performance to be achieved.

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