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Proceedings Paper

Construction Of Low Noise Correlation Filters
Author(s): Robert R. Kallman
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Paper Abstract

Synthetic discriminant functions (SDF's) for matched filters have potential use for pattern recognition. However, these filters have been plagued with a low signal-to-noise ratio (SNR); i.e., these filters have no trouble correlating very well with true targets, but very often give high (even major) correlations with false targets. In fact, numerical experiments by the author and others show that the standard recipe for manufacturing SDF's gives filters with an SNR close to 1.00, even on a training set of imagery which has been edge-enhanced and energy-normalized. The author has introduced a new recipe for manufacturing SDF filters. These filters have an SNR of over 7.37 against their training sets and have proved to be very accurate in picking targets from extremely cluttered backgrounds, in fact much more accurate than the filter made from the standard recipe. The purpose of this note is to concisely recall the author's new recipe for these filters and to report on recent numerical experiments in which the difference in performance between filters made from the new and old recipes is, if anything, even more pronounced.

Paper Details

Date Published: 15 October 1986
PDF: 4 pages
Proc. SPIE 0638, Hybrid Image Processing, (15 October 1986); doi: 10.1117/12.964280
Show Author Affiliations
Robert R. Kallman, North Texas State University (United States)

Published in SPIE Proceedings Vol. 0638:
Hybrid Image Processing
David P. Casasent; Andrew G. Tescher, Editor(s)

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