Asymptotic performance analysis for object recognition in clutter

Dmitri Bitouk; Michael I. Miller; Laurent Younes

doi:10.1117/12.487051

16 September 2003 Asymptotic performance analysis for object recognition in clutter

Dmitri Bitouk, Michael I. Miller, Laurent Younes

Proceedings Volume 5094, Automatic Target Recognition XIII; (2003) https://doi.org/10.1117/12.487051
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

Dmitri Bitouk, Michael I. Miller, and Laurent Younes "Asymptotic performance analysis for object recognition in clutter", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.487051

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