NDLI: A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Wysoski, S.G. Lamar, M.V. Kuroyanagi, S. Iwata, A.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nagoya Inst. of Technol., Japan (Wysoski, S.G.; Lamar, M.V.; Kuroyanagi, S.; Iwata, A.)
Abstract	The appropriate selection of feature extraction method plays an important role in designing a pattern recognition system. The proper representation of features contributes to a significant improvement of the classifier performance and also to the reduction of time processing. The purpose of this study is to present a description of hand's posture features based on boundary histograms. The use of histograms aims to deal with two problems: the chain small magnitude circular-shift problem caused by posture rotation and, to attenuate the non-linearity caused by shape differences when performing gesture postures. We also present a fast search start point algorithm for the boundary chain that gives a rotation invariance property to the system. The performance was evaluated using 26 postures of American Sign Language, and a comparison with other algorithms is presented. As result we obtained a robust method to be used in largescale applications using neural networks.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	2137
Ending Page	2141
File Size	370066
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1199054
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Histograms Neural networks Shape Humans Robustness Pattern recognition Eyes Fingers Data mining Principal component analysis
Content Type	Text
Resource Type	Article

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