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by Eun-jung Holden, Robyn Owens, Geoffrey G. Roy, Member Ieee, Member Ieee
http://www.cs.uwa.edu.au:80/~eunjung/mypubs/PAMIpaperA.ps.gz
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Abstract:
The gesture recognition process, in general, may be divided into two stages: motion sensing, which extracts useful data from hand motion; and the classification process, which classifies the motion sensing data as gestures. We have developed the visionbased Hand Motion Understanding (HMU) system that recognises static and dynamic Australian Sign Language (Auslan) signs by extracting and classifying 3D hand configuration data from the visual input. The HMU system uses a combination of a 3D hand tracker for motion sensing, and an adaptive fuzzy expert system for classification. This paper explains the 3D hand tracker that extracts the changes in the 21 degrees-of-freedom parameters of the hand from a sequence of images. The tracker performance is successfully demonstrated by tracking the hand motions appearing in Auslan sign sequences.
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