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Proceedings Paper

Using pseudorandom phase-only encoding to approximate fully complex distortion-invariant filters
Author(s): Laurence G. Hassebrook; Michael E. Lhamon; Raymond C. Daley; Robert W. Cohn; Minhua Liang
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Paper Abstract

In this paper, we introduce a unified model-based pattern recognition approach that can be formulated into a variety of techniques to be used for a variety of applications. Complex phasor addition and cancellation are incorporated into the design of filter(s) to perform implicit logical operations using linear correlation operators. These implicit logical operations are suitable to implement high-level gray-scale morphological transformations of input images. In this way we effectively project nonlinear decision boundaries into the input signal space yet maintain the mathematical simplicity of linear filter designs. We apply this approach to the automatic distortion- and intensity-invariance object recognition problem. We introduce a set of shape operators or complex filters that are logically structured into a filter bank architecture to accomplish the distortion and intensity-invariant system. This synthesized complex filter bank is optimally sensitive to fractal noise representing natural scenery. The sensitivity is optimized for a specific fractal parameter range using the Fisher discriminant. The output responses of the proposed system are shown for target, clutter, and pseudo-target inputs to represent its discrimination and generalization capability in the presence of distortion and intensity variations.

Paper Details

Date Published: 1 March 1994
PDF: 8 pages
Proc. SPIE 2237, Optical Pattern Recognition V, (1 March 1994); doi: 10.1117/12.169425
Show Author Affiliations
Laurence G. Hassebrook, Univ. of Kentucky (United States)
Michael E. Lhamon, Univ. of Kentucky (United States)
Raymond C. Daley, Univ. of Kentucky (United States)
Robert W. Cohn, Univ. of Louisville (United States)
Minhua Liang, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 2237:
Optical Pattern Recognition V
David P. Casasent; Tien-Hsin Chao, Editor(s)

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