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Proceedings Paper

Combining multiple correlators using neural networks
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Paper Abstract

Designing a pattern classifier remains a difficult problem especially in the presence of noise and other degradations. Combination of multiple classifiers appears to be a good way of retaining the strengths of different classifiers while avoiding their weaknesses. Different combination schemes were proposed in the literature. As a special case of combining multiple classifiers, we consider combining correlators. Correlators are attractive for use in Automatic Target Recognition systems. Many correlation filter designs have been developed, each with its own features. Some filter designs maximize noise tolerance but do not provide sharp peaks. On the other hand, some correlation filters yield sharp correlation peaks but are overly sensitive to input noise. In this research effort, we explore the use of artificial neural network as a tool for combining correlators. Results of this implementation show improvements and indicate that combination of multiple correlators can potentially improve the classification performance.

Paper Details

Date Published: 27 March 1997
PDF: 6 pages
Proc. SPIE 3073, Optical Pattern Recognition VIII, (27 March 1997); doi: 10.1117/12.270388
Show Author Affiliations
Mohamed Alkanhal, Carnegie Mellon Univ. (Saudi Arabia)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)
Abhijit Mahalanobis, Hughes Missile Systems Co. (United States)

Published in SPIE Proceedings Vol. 3073:
Optical Pattern Recognition VIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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