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Proceedings Paper

Linear Algebra Techniques For Pattern Recognition: Feature Extraction Case Studies
Author(s): David Casasent
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Paper Abstract

Many linear algebra operations, matrix inversions, etc. are required in pattern recognition as well as in signal processing. In this paper, we concentrate on feature extraction pattern recognition techniques (specifically a chord distribution and a moment feature space). For these two case studies, we note the various linear algebra operations required in distortion-invariant pattern recognition. Systolic processors can easily perform all reauired linear algebra functions.

Paper Details

Date Published: 28 November 1983
PDF: 7 pages
Proc. SPIE 0431, Real-Time Signal Processing VI, (28 November 1983); doi: 10.1117/12.936466
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)


Published in SPIE Proceedings Vol. 0431:
Real-Time Signal Processing VI
Keith Bromley, Editor(s)

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