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Proceedings Paper

Localized feature selection to maximize discrimination
Author(s): Kenneth A. Duell; Mark O. Freeman
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Paper Abstract

We present an automatic method of designing correlation filters for pattern recognition that are composed of select local features (i.e., small parts of a reference object). The local features are selected for their ability to discriminate between the reference object and other known objects or patterns. In the basic localized feature selection problem, we design a correlation filter from a single optimal local feature. In the general localized feature selection problem, we design a correlation filter composed of several local features. We show that the discrimination ability of a correlation filter designed form properly selected local features is actually greater than the discrimination ability of a traditional matched filter.

Paper Details

Date Published: 1 November 1991
PDF: 12 pages
Proc. SPIE 1564, Optical Information Processing Systems and Architectures III, (1 November 1991); doi: 10.1117/12.49693
Show Author Affiliations
Kenneth A. Duell, Univ. of Colorado/Boulder (United States)
Mark O. Freeman, Univ. of Colorado/Boulder (United States)


Published in SPIE Proceedings Vol. 1564:
Optical Information Processing Systems and Architectures III
Bahram Javidi, Editor(s)

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