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Proceedings Paper

Autonomous learning in gesture recognition by using lobe component analysis
Author(s): Jian Lu; Juyang Weng
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Paper Abstract

Gesture recognition is a new human-machine interface method implemented by pattern recognition(PR).In order to assure robot safety when gesture is used in robot control, it is required to implement the interface reliably and accurately. Similar with other PR applications, 1) feature selection (or model establishment) and 2) training from samples, affect the performance of gesture recognition largely. For 1), a simple model with 6 feature points at shoulders, elbows, and hands, is established. The gestures to be recognized are restricted to still arm gestures, and the movement of arms is not considered. These restrictions are to reduce the misrecognition, but are not so unreasonable. For 2), a new biological network method, called lobe component analysis(LCA), is used in unsupervised learning. Lobe components, corresponding to high-concentrations in probability of the neuronal input, are orientation selective cells follow Hebbian rule and lateral inhibition. Due to the advantage of LCA method for balanced learning between global and local features, large amount of samples can be used in learning efficiently.

Paper Details

Date Published: 27 February 2007
PDF: 10 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970S (27 February 2007); doi: 10.1117/12.705889
Show Author Affiliations
Jian Lu, National Institute of Occupational Safety and Health (Japan)
Juyang Weng, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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