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Proceedings Paper

Invariant pattern recognition via higher order preprocessing and backprop
Author(s): Jon P. Davis; William A. Schmidt
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Paper Abstract

Higher-order neural networks are a variation of the standard back-propagation neural network, using geometrically motivated nonlinear combinations of scene pixel values as a feature space. The effects of varying feature size (in number of pixels), scene size, number of features, summation-over-scene versus maximum-over-scene, and number of hidden layers, are examined.

Paper Details

Date Published: 1 August 1991
PDF: 8 pages
Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); doi: 10.1117/12.45018
Show Author Affiliations
Jon P. Davis, Naval Air Development Ctr. (United States)
William A. Schmidt, Naval Air Development Ctr. (United States)

Published in SPIE Proceedings Vol. 1469:
Applications of Artificial Neural Networks II
Steven K. Rogers, Editor(s)

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