Generalized linear correlation filters

Andres Rodriguez; B. V. K. Vijaya Kumar

doi:10.1117/12.2015380

20 May 2013 Generalized linear correlation filters

Proceedings Volume 8744, Automatic Target Recognition XXIII; 874401 (2013) https://doi.org/10.1117/12.2015380
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

We present two generalized linear correlation filters (CFs) that encompass most of the state-of-the-art linear CFs. The common criteria that arc used in linear CF design are the mean squared error (MSE), output noise variance (ONV), and average similarity measure (ASM). We present a simple formulation that uses an optimal tradeoff among these criteria both constraining and not constraining the correlation peak value, and refer to them as generalized Constrained Correlation Filter (CCF) and Unconstrained Couelation Filter (UCF). We show that most state-of-the-art linear CFs arc subsets of these filters. We present a technique for efficient UCF computation. We also introduce the modified CCF (mCCF) that chooses a unique correlation peak value for each training image, and show that mCCF usually outperforms both UCF and CCF.

Citation Download Citation

Andres Rodriguez and B. V. K. Vijaya Kumar "Generalized linear correlation filters", Proc. SPIE 8744, Automatic Target Recognition XXIII, 874401 (20 May 2013); https://doi.org/10.1117/12.2015380

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