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Proceedings Paper

Feature space trajectory neural net classifier
Author(s): Leonard Neiberg; David P. Casasent
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Paper Abstract

A new classifier neural network is described for distortion-invariant multi-class pattern recognition. The input analog neurons are a feature space. All distorted aspect views of one object are described by a trajectory in feature space. Classification of test data involves calculation of the closest feature space trajectory. Pose estimation is achieved by determining the closest line segment on the closest trajectory. Rejection of false class clutter is demonstrated. Comparisons are made to other neural network classifiers, including a radial basis function and a new standard backpropagation neural net. The shapes of the different decision surfaces produced by our feature space trajectory classifier are analyzed.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205141
Show Author Affiliations
Leonard Neiberg, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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