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

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

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available