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Proceedings Paper

Fourier-Transform Feature-Space Studies
Author(s): David Casasent; Vinod Sharma
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Paper Abstract

A hierarchial multi-level feature-space pattern recognition system is described. Multi-class distortion-invariant object identification is the purpose of this study. Attention is given to dimensionality reduction (to simplify computations) and to the use of non-unitary transformations (to achieve discrimination). A Fourier transform feature space is used. However, our basic hierarchial concepts, our theoretical analysis, and our general conclusions are applicable to other feature spaces. The use of intensity versus phase features is studied and the performance of our system in the presence of noise is studied. Quantitative experimental data on 2 two-class pattern recognition databases are provided.

Paper Details

Date Published: 16 February 1984
PDF: 7 pages
Proc. SPIE 0449, Intelligent Robots: 3rd Intl Conf on Robot Vision and Sensory Controls, (16 February 1984); doi: 10.1117/12.939217
Show Author Affiliations
David Casasent, Carnegie-Mellon University (United States)
Vinod Sharma, Carnegie-Mellon University (United States)

Published in SPIE Proceedings Vol. 0449:
Intelligent Robots: 3rd Intl Conf on Robot Vision and Sensory Controls
David P. Casasent; Ernest L. Hall, Editor(s)

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