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Proceedings Paper

Classification Of Multi-Classed Stochastic Images Buried In Additive Noise
Author(s): Zu-Han Gu; Sing H. Lee
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Paper Abstract

The Optimal Correlation Filter for the discrimination or classification of multi-class stochastic images buried in additive noise is designed. We consider noise in images as the (K+1)th class of stochastic image so that the K-class with noise problem becomes a problem of (K+1)-classes: K-class without noise plus the (K+1)th class of noise. Experimental verifications with both low frequency background noise and high fre-quency shot noise show that the new filter design is reliable.

Paper Details

Date Published: 8 January 1987
PDF: 11 pages
Proc. SPIE 0700, 1986 Intl Optical Computing Conf, (8 January 1987); doi: 10.1117/12.936937
Show Author Affiliations
Zu-Han Gu, University of California at San Diego (United States)
Sing H. Lee, University of California at San Diego (United States)

Published in SPIE Proceedings Vol. 0700:
1986 Intl Optical Computing Conf
Asher A. Friesem; Emanuel Marom; Joseph Shamir, Editor(s)

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