Optimal separable correlation filters

Frank E. McFadden

doi:10.1117/12.477014

25 July 2002 Optimal separable correlation filters

Frank E. McFadden

Proceedings Volume 4726, Automatic Target Recognition XII; (2002) https://doi.org/10.1117/12.477014
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Separable filters, because they are specified separately in each dimension, require less memory space and present opportunities for faster computation. Mahalanobis and Kumar1 presented a method for deriving separable correlation filters, but the filters were required to satisfy a restrictive assumption, and were thus not fully optimized. In this work, we present a general procedure for deriving separable versions of any correlation filter, using singular value decomposition (SVD), and prove that this is optimal for separable filters based on the Maximum Average Correlation Height (MACH) criterion. Further, we show that additional separable components may be used to improve the performance of the filter, with only a linear increase in computational and memory space requirements. MSTAR data is used to demonstrate the effects on sharpness of correlation peaks and locational precision, as the number of separable components is varied.

Citation Download Citation

Frank E. McFadden "Optimal separable correlation filters", Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); https://doi.org/10.1117/12.477014

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