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Proceedings Paper

Pruning of training sets used for synthetic discriminant function filter design by relaxing the correlation plane constraints
Author(s): Bhagavatula Vijaya Kumar; Srinivas Bollapragada
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Paper Abstract

Conventional synthetic discriminant function (SDF) filter designs use all available training images by requiring that the designed SDF filter yield prespecified values at the origin of the correlation plane when various training images are used in the input. In this paper, we show that we can reduce the number of training images being used and improve the filter performance by relaxing the correlation plane constraints when threshold detection is employed in the output correlation plane.

Paper Details

Date Published: 1 March 1994
PDF: 5 pages
Proc. SPIE 2237, Optical Pattern Recognition V, (1 March 1994); doi: 10.1117/12.169420
Show Author Affiliations
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)
Srinivas Bollapragada, Carnegie Mellon Univ. (United States)

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

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