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Proceedings Paper

Hierarchical Fisher And Moment-Based Pattern Recognition
Author(s): David Casasent; R. Lee Cheatham
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Paper Abstract

A two-level feature extraction classifier using a geometrical-moment feature space is described for multi-class distortion-invariant pattern recognition. The first-level classifier provides object class and aspect estimates using multi-class Fisher projections and optimized two-class Fisher projections in a hierarchical classifier. Aspect estimates are provided from ratios of the computed moments. The second-level classifier provides the final class estimate, distortion parameter estimates and the confidence of the estimates. Extensive test results on a ship image database are presented.

Paper Details

Date Published: 4 December 1984
PDF: 8 pages
Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); doi: 10.1117/12.944841
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)
R. Lee Cheatham, Carnegie-Mellon University (United States)

Published in SPIE Proceedings Vol. 0504:
Applications of Digital Image Processing VII
Andrew G. Tescher, Editor(s)

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