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Kokubunji, Tokyo, Japan
Oct. 26, 2004 to Oct. 29, 2004
ISBN: 0-7695-2187-8
pp: 100-105
Nianjun Liu , University of Queensland
Brian C. Lovell , University of Queensland
Peter J. Kootsookos , University of Queensland
Richard I. A. Davis , University of Queensland
ABSTRACT
Hidden Markov models using the Fully-Connected, Left-Right and Left-Right Banded model structures are applied to the problem of alphabetical letter gesture recognition. We examine the effect of training techniques, in particular the Baum-Welch and Viterbi Path Counting techniques, on each of the model structures. We show that recognition rates improve when moving from a Fully-Connected model to a Left-Right model and a Left-Right Banded ?staircase? model with peak recognition rates of 84.8%, 92.31% and 97.31% respectively. The Left-Right Banded model in conjunction with the Viterbi Path Counting present the best performance. Direct calculation of model parameters from analysis of the physical system was also tested, yielding a peak recognition rate of 92%, but the simplicity and efficiency of this approach is of interest.
INDEX TERMS
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CITATION
Nianjun Liu, Brian C. Lovell, Peter J. Kootsookos, Richard I. A. Davis, "Model Structure Selection and Training Algorithms for an HMM Gesture Recognition System", IWFHR, 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition 2004, pp. 100-105, doi:10.1109/IWFHR.2004.68
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