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Proceedings Paper

Minace filter tests on the Comanche IR database
Author(s): David Casasent; Rohit Patnaik
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Paper Abstract

This paper presents our IR automatic target recognition (ATR) work on the Comanche database using the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF). The Comanche database contains real IR data of eight targets with aspect view and thermal state variations. We consider recognition of six of these targets and we consider rejecting two targets (confusers) and clutter. To handle the full 360° range of aspect view in Comanche data, we use a set of Minace filters for each object; each filter should recognize the object in some angular range. We use our autoMinace algorithm that uses a training and a validation set to select the Minace filter parameter c (which selects emphasis on recognition or discrimination) and to select the training set images to be included in the filter, so that the filter can achieve both good recognition and good confuser and clutter rejection performance. No confuser, clutter, or test set data are present in the training or the validation set. Use of the peak-to-correlation energy (PCE) ratio is found to perform better than the use of the correlation peak height metric. The use of circular versus linear correlations is addressed; circular correlations require less storage and fewer online computations and are thus preferable.

Paper Details

Date Published: 9 April 2007
PDF: 11 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740H (9 April 2007); doi: 10.1117/12.719069
Show Author Affiliations
David Casasent, Carnegie Mellon Univ. (United States)
Rohit Patnaik, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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