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Proceedings Paper

Asymptotic performance analysis for object recognition in clutter
Author(s): Dmitri Bitouk; Michael I. Miller; Laurent Younes
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Paper Abstract

This paper analyzes the performance of ATR algorithms in clutter. The variability of target type and pose is accommodated by introducing a deformable template for every target type, with low-dimensional groups of geometric transformations representing position and pose. Signature variation of targets is taken into account by expanding deformable templates into robust deformable templates generated from the template and a linear combination of PCA elements, spanning signature intensities. Detection and classification performance is characterized using ROC analysis. Asymptotic expressions for probabilities of recognition errors are derived, yielding asymptotic error rates. The results indicate that the asymptotic error probabilities depend upon a parameter, which characterizes the separation between the true target and the most similar but incorrect one. It is shown that the asymptotic expressions derived almost accurately predict performance of detection and identification of targets occluded by natural clutter.

Paper Details

Date Published: 16 September 2003
PDF: 8 pages
Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.487051
Show Author Affiliations
Dmitri Bitouk, Johns Hopkins Univ. (United States)
Michael I. Miller, Johns Hopkins Univ. (United States)
Laurent Younes, Ecole Normale Superieure de Cachan (France)

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

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