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Proceedings Paper

Minimum mean square error filter for pattern recognition with spatially disjoint signal and scene noise
Author(s): Philippe Refregier; Bahram Javidi; Guanshen Zhang; Farokh Parchekani
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Paper Abstract

A minimum mean square error filter for pattern recognition problems with input scene noise that is spatially disjoint (or nonoverlapping) with the target is described. The filter is designed to locate the target by producing a delta function output at the target position. The filter minimizes the mean square of the difference between the desired output delta function and the filter output in response to a noisy input data. We show that the filter output has a well defined peak and small sidelobes in the presence of spatially disjoint target and scene noise.

Paper Details

Date Published: 9 November 1993
PDF: 5 pages
Proc. SPIE 2026, Photonics for Processors, Neural Networks, and Memories, (9 November 1993); doi: 10.1117/12.163619
Show Author Affiliations
Philippe Refregier, Thomson-CSF (France)
Bahram Javidi, Univ. of Connecticut (United States)
Guanshen Zhang, Univ. of Connecticut (United States)
Farokh Parchekani, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 2026:
Photonics for Processors, Neural Networks, and Memories
Stephen T. Kowel; William J. Miceli; Joseph L. Horner; Bahram Javidi; Stephen T. Kowel; William J. Miceli, Editor(s)

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