Minimum average correlation energy (MACE) prefilter networks for automatic target recognition

Gregory L. Hobson; S. Richard F. Sims; Paul D. Gader; James M. Keller

doi:10.1117/12.181037

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29 July 1994 Minimum average correlation energy (MACE) prefilter networks for automatic target recognition

Gregory L. Hobson, S. Richard F. Sims, Paul D. Gader, James M. Keller

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Proceedings Volume 2234, Automatic Object Recognition IV; (1994) https://doi.org/10.1117/12.181037
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

Minimum average correlation energy (MACE) filters have been shown to be an effective generalization of the synthetic discriminant function (SDF) approach to automatic target recognition. The MACE filter has the advantage of having a very low false alarm rate, but suffers from a diminished probability of detection. Several generalizations have recently been proposed to maintain the low false alarm rate while increasing the probability of detection. The mathematical formulation of the MACE filter can be decomposed into a linear `prefilter' followed by an SDF-like operation. It is the prefiltering portion of the MACE which accounts for the low false alarm rate. In this paper, we insert a nonlinearity in this process by replacing the SDF portion of the operation by a neural network and show that we can increase the probability of detection without sacrificing low false alarm rates. This approach is demonstrated on a standard multiaspect image set and compared to the MACE and its generalizations.

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Gregory L. Hobson, S. Richard F. Sims, Paul D. Gader, and James M. Keller "Minimum average correlation energy (MACE) prefilter networks for automatic target recognition", Proc. SPIE 2234, Automatic Object Recognition IV, (29 July 1994); https://doi.org/10.1117/12.181037

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