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Proceedings Paper

Generalized linear correlation filters
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Paper 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.

Paper Details

Date Published: 20 May 2013
PDF: 12 pages
Proc. SPIE 8744, Automatic Target Recognition XXIII, 874401 (20 May 2013); doi: 10.1117/12.2015380
Show Author Affiliations
Andres Rodriguez, Air Force Research Lab. (United States)
B. V. K. Vijaya Kumar, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 8744:
Automatic Target Recognition XXIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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