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Proceedings Paper

Asymptotic performance of ATR in infrared images
Author(s): Can Ceritoglu; Dmitri Bitouk; Michael I. Miller; Harry A. Schmitt
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Paper Abstract

In this study, the asymptotic performance analysis for target detection-identification through Bayesian hypothesis testing in infrared images is presented. In the problem, probabilistic representations in terms of Bayesian pattern-theoretic framework is used. The infrared clutter is modelled as a second-order random field. The targets are represented as rigid CAD models. Their infinite variety of pose is modelled as transformations on the templates. For the template matching in hypothesis testing, a metric distance, based on empirical covariance, is used. The asymptotic performance of ATR algorithm under this metric and Euclidian metric is compared. The receiver operating characteristic (ROC) curves indicate that using the empirical covariance metric improves the performance significantly. These curves are also compared with the curves based on analytical expressions. The analytical results predict the experimental results quite well.

Paper Details

Date Published: 16 September 2003
PDF: 10 pages
Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.487383
Show Author Affiliations
Can Ceritoglu, John Hopkins Univ. (United States)
Dmitri Bitouk, John Hopkins Univ. (United States)
Michael I. Miller, Johns Hopkins Univ. (United States)
Harry A. Schmitt, Raytheon Missile Systems (United States)

Published in SPIE Proceedings Vol. 5094:
Automatic Target Recognition XIII
Firooz A. Sadjadi, Editor(s)

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