Developing Task-Specific RBF Hand Gesture Recognition

Howell, A.J., Sage, K. and Buxton, H. (2004) Developing Task-Specific RBF Hand Gesture Recognition. In: Volpe, Gualtiero and Camurri, Antonio (eds.) Gesture-Based Communication in Human-Computer Interaction. Lecture Notes in Computer Science, 2915 . Springer Berlin / Heidelberg, Berlin, Germany, pp. 269-276. ISBN 9783540210726

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Abstract

In this paper we develop hand gesture learning and recognition techniques to be used in advanced vision applications, such as the ActIPret system for understanding the activities of expert operators for education and training. Radial Basis Function (RBF) networks have been developed for reactive vision tasks and work well, exhibiting fast learning and classification. Specific extensions of our existing work to allow more general 3-D activity analysis reported here are: 1) action-based representation in a hand frame-of-reference by pre-processing of the trajectory data; 2) adaptation of the time-delay RBF network scheme to use this relative velocity information from the 3-D trajectory information in gesture recognition; and 3) development of multi-task support in the classifications by exploiting prototype similarities extracted from different combinations of direction (target tower) and height (target pod) for the hand trajectory.

Item Type: Book Section
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User: Chris Keene
Date Deposited: 11 Feb 2008
Last Modified: 30 Nov 2012 16:51
URI: http://sro.sussex.ac.uk/id/eprint/1263
Google Scholar:4 Citations
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