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Proceedings Paper

Robust shape recognition using space-perturbation neural networks
Author(s): Ming Ji; Huihuang Chen; Zhenkang Shen
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Paper Abstract

In this paper we present a neural network approach to shape recognition. The primary focus is the development of an effective representation method to increase the degree of robustness in recognition of shapes which may be blurred by noise. A space-perturbation neural network (SPNN) is described which is characterized by two important properties. (1) The network can be trained using error back-propagation with only noise-free data, which avoids the convergence problem due to possibly large variance in each shape class. (2) The space- perturbation arrangement enables the network to discover class features independent of random variations in shape and, hence, not blurred by random variations in the input. As a recognition task, the classification of four closed planar shapes was chosen. For comparison, the neural network classifier based upon extended training technique was implemented to perform the same task. Performance evaluation over 4000 testing shapes from various blur conditions showed that (1) the SPNN outperforms the extended training approach, especially when blur is present -- a median 25% reduction in the number of misclassification is achieved for the noisy case; (2) the SPNN performs about the same as the extended training approach for the noise-free case; and (3) the SPNN can be trained with a much reduced time consumption in comparison to the extended training scheme.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140009
Show Author Affiliations
Ming Ji, Changsha Institute of Technology (China)
Huihuang Chen, Changsha Institute of Technology (China)
Zhenkang Shen, Changsha Institute of Technology (China)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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