Minace filter tests on the Comanche IR database

David Casasent; Rohit Patnaik

doi:10.1117/12.719069

9 April 2007 Minace filter tests on the Comanche IR database

David Casasent, Rohit Patnaik

Proceedings Volume 6574, Optical Pattern Recognition XVIII; 65740H (2007) https://doi.org/10.1117/12.719069
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

David Casasent and Rohit Patnaik "Minace filter tests on the Comanche IR database", Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740H (9 April 2007); https://doi.org/10.1117/12.719069

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