
Proceedings Paper
Nonlinear Fourier correlationFormat | Member Price | Non-Member Price |
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Paper Abstract
Fourier correlators perform space-invariant linear filtering on all input points, so they can
identify and locate patterns in parallel. Each output point is a weighted sum of
components of the Fourier transform of the input, so the discriminants used are inherently
linear. As most practical problems are not linearly discriminable, that causes a problem.
This paper describes a quite general solution involving nonlinear combining of
nonlinearly processed outputs from multiple Fourier masks. The design of the masks and
nonlinearities allows very powerful nonlinear discrimination that preserves the space-invariant
feature that makes Fourier correlators attractive. Given a set of target-class
images, henceforth referred to as the training set or trainers, the algorithm developed
herein computes an ordered set of classifier filters - Generalized Matched Filters (GMFs)
threshold values. An unlabeled image is applied to the classifier filter set, hereafter
referred to as super-generalized matched filter (SGMF). If the peak response of any of the
classifier filters (GMFs) to the unlabeled test image exceeds the threshold level the
decision is made in favor of labeling the image as target-class otherwise it is labeled non-target-
class.
Paper Details
Date Published: 13 April 2009
PDF: 11 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 73400A (13 April 2009); doi: 10.1117/12.818256
Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)
PDF: 11 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 73400A (13 April 2009); doi: 10.1117/12.818256
Show Author Affiliations
Kaveh Heidary, Alabama A&M Univ. (United States)
H. John Caulfield, Alabama A&M Univ. Research Institute (United States)
Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)
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