A nonlinear connectionist approach to performance enhancement of correlation filters

Timothy Brucks; Jack G. Riddle; Peter J. VanMaasdam

doi:10.1117/12.485815

16 September 2003 A nonlinear connectionist approach to performance enhancement of correlation filters

Timothy Brucks, Jack G. Riddle, Peter J. VanMaasdam

Proceedings Volume 5094, Automatic Target Recognition XIII; (2003) https://doi.org/10.1117/12.485815
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Correlation-based filters (e.g MACE, MACH) have been widely employed for automatic target acquisition. In general, a bank of filters is developed wherein each filter is trained to respond to a particular range of conditions (such as aspect angle). Individual filter outputs are utilized to determine a best match between objects in a scene and the training information. However, it is not uncommon for discrete clutter objects to correlate well with an individual filter, resulting in an unacceptable false alarm rate (FAR). It is the authors' hypothesis that although a clutter event may correlate well with an individual filter, there are discernable differences in the way clutter and targets correlate across the bank of filters. In this paper, the authors investigate a connectionist based approach that combines the individual filter outputs in a non-linear manner for improved performance. Particular attention is given to designing the correlation filter constraints in conjunction with the combination approach to optimize performance.

Citation Download Citation

Timothy Brucks, Jack G. Riddle, and Peter J. VanMaasdam "A nonlinear connectionist approach to performance enhancement of correlation filters", Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); https://doi.org/10.1117/12.485815

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