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Proceedings Paper

Neural net selection methods for Gabor transform detection filters
Author(s): David P. Casasent; John Scott Smokelin
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Paper Abstract

New Gabor transform (GT) filters to detect candidate object locations independent of the object class, object distortions, and for low contrast objects in clutter are described. A new neural network (NN) technique is described to automate selection of GT parameters and to combine multiple Gabor functions (GFs) into once composite macro GF detection filter. Fusion of real and imaginary GT filter outputs is used to reduce false alarms, (PFA), while maintaining high detection rates (PD). Test results on the TRIM-2 database are provided.

Paper Details

Date Published: 20 August 1993
PDF: 15 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150130
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
John Scott Smokelin, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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