| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 94 === Gesture recognition is needed for a variety of applications such as human-computer interfaces, communication aids for the deaf, etc. In this thesis, we present a SOMART system for the recognition of hand gestures. The sequence of a hand gesture is first projected into a 2-dimensional trajectory on a self-organizing feature map (SOM). Then the problem of recognizing hand gestures is transformed to the problem of recognizing hand-written characters. The adaptive resonance theory (ART) algorithm generates multiple templates for each hand gesture. Finally, an unknown gesture is classified to be the gesture with the maximum similarity in the vocabulary via a template matching technique. In addition, the conception of SOMART system can also apply to hand movement trajectory recognition.
A database consisted of 47 static hand gestures, 103 dynamic hand gestures, and eight movement trajectories was tested to demonstrate the performance of the proposed method. The average recognition rate of static hand gestures is 92%, the recognition rate of dynamic hand gestures is 88%, and 99% for hand movement trajectories.
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