Recent advances in correlation filter theory and application

Abstract

Advanced correlation filters (CFs) were introduced over three decades ago to offer distortion-tolerant object recognition and are used in applications such as automatic target recognition (ATR) and biometric recognition. Some of the advances in CF design include minimum average correlation energy (MACE) filters that produce sharp correlations and offer excellent discrimination, optimal tradeoff synthetic discriminant function (OTSDF) filters that allow the filter designer to control the tradeoff between peak sharpness and noise tolerance, maximum average correlation height (MACH) filter that removes correlation peak constraints to reduce filter design complexity and quadratic correlation filters (QCFs) that extend the linear CFs to include second-order nonlinearity. In this paper, we summarize two recent major advances in CF design. First is the introduction of maximum margin correlation filters (MMCFs) that combine the excellent localization properties of CFs with the very good generalization abilities of support vector machines (SVMs). Second is the introduction of zero-aliasing correlation filters (ZACFs) that eliminate the aliasing in CF design due to the circular correlation caused by the use of discrete Fourier transforms (DFTs).