Abstract

Prior work on generalized matched filters (GMFs) was limited to eighty points and a 1-D signal. We show here how to calculate arbitrarily large GMFs. We then compare GMFs with other common pattern recognition filters for one particular image using computer simulation. The GMF appears to offer improvements in terms of smaller within-class variability and greater between-class separation relative to matched filters. In addition, the GMF permits far more accurate location of the object in the input scene than the matched filter.

© 1982 Optical Society of America

Computer recognition of 2-D patterns using generalized matched filters: erratum

H. J. Caulfield and M. H. Weinberg
Appl. Opt. 21(19) 3416-3416 (1982)

Hybrid optical processor for pattern recognition and classification using a generalized set of pattern functions

James R. Leger and Sing H. Lee
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Pattern recognition with quantized computer-generated filters

Juan Campos, Francina Turon, and María J. Yzuel
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Z. Q. Wang, W. A. Gillespie, C. M. Cartwright, and C. Soutar
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Optical pattern recognition using circular harmonic expansion

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