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Proceedings Paper

Feature space trajectory neural net classifier: 8-class distortion-invariant tests
Author(s): Leonard Neiberg; David P. Casasent; Robert J. Fontana; Jeffrey E. Cade
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Paper Abstract

A novel neural network for distortion-invariant pattern recognition is described. Image regions of interest are determined using a detection stage, each region is then enhanced (the steps used are detailed), features are extracted (new Gabor wavelet features are used), and these features are used to classify the contents of each input region. A new feature space trajectory neural network (FST NN) classifier is used. A new 8 class database is used, a new multilayer NN to calculate the distance measures necessary is detailed, its low storage and on-line computational load requirements are noted. The ability of the adaptive FST algorithm to reduce network complexity while achieving excellent performance is demonstrated. The clutter rejection ability of this neural network to reject false alarm inputs is demonstrated, and time-history processing to further reduce false alarms is discussed. Hardware and commercial realizations are noted.

Paper Details

Date Published: 3 October 1995
PDF: 16 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222707
Show Author Affiliations
Leonard Neiberg, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)
Robert J. Fontana, Multispectral Solutions, Inc. (United States)
Jeffrey E. Cade, Multispectral Solutions, Inc. (United States)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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