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Proceedings Paper

Pattern Recognition Using Wigner Distribution Function
Author(s): B.V.K. Vijaya Kumar; Christopher Carroll
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Paper Abstract

The Wigner Distribution Function (WDF) is an excellent tool for characterizing time varying signals. Several approaches have been recently suggested for optical implementation of Wigner Distri-bution Function of signals. In this paper, we report on our simulation efforts to use WDF for signal and image classification. We also relate WDF to other conventional time-frequency representa-tions of a signal such as Ambiguity Function. We develop analytical results quantifying the performance of WDF in detection problems. These analytical results are compared to the experimentally observed results. Signal parameter estimation methods that use WDF are also introduced. Extensions of WDF approach to higher dimensions (as in the case of images) are briefly outlined.

Paper Details

Date Published: 15 April 1983
PDF: 7 pages
Proc. SPIE 0422, 10th Intl Optical Computing Conf, (15 April 1983); doi: 10.1117/12.936140
Show Author Affiliations
B.V.K. Vijaya Kumar, Carnegie-Mellon University (United States)
Christopher Carroll, Carnegie-Mellon University (United States)

Published in SPIE Proceedings Vol. 0422:
10th Intl Optical Computing Conf
Sam Horvitz, Editor(s)

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