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Proceedings Paper

Feature space trajectory neural net classifier: confidences and thresholds for clutter and low-contrast objects
Author(s): Leonard Neiberg; David P. Casasent; Ashit Talukder
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Paper Abstract

The feature space trajectory neural net is reviewed. Its advantages over other classifiers are noted; it allows use of smaller training sets, large numbers of hidden layer neurons, low on- line computational loads, higher-order decision surfaces, the ability to reject false class input (clutter) data, etc. New test results on its 3D distortion-invariant classification performance are provided using a larger object and clutter database, input object contrast differences, a new preprocessing algorithm, and a new feature space. We note the problems with other neural net classifiers that our architecture and algorithm overcomes, the use of different distance thresholds and confidence measures to improve performance, advantages of using adjunct features, and numerous new test results.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235933
Show Author Affiliations
Leonard Neiberg, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)
Ashit Talukder, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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