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Proceedings Paper

Position-invariant target detection by a neural net
Author(s): Jon P. Davis; William A. Schmidt
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Paper Abstract

We investigated the possibility of using an artificial neural network as a translation invariant target detector. The one-dimensional target detection model was a linear array of 20 pixels of which three were unity and the remainder were zero. Several multi-layer back progagation networks were able to distinguish a target consisting of three contiguous pixels from a nontarget three non-contiguous pixels. Under-constrained models were not trainable. A detailed analysis was done of one network with a small number of connections. The network solution appeared to be similar to a triplet correlat ion funct ion. 1.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21163
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. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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