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Proceedings Paper

Object discrimination using neural networks
Author(s): James R. Dyvig
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Paper Abstract

Classification of object signatures is not a trivial task. An object's signature only represents one particular aspect of the object's physical characteristics such as fluid density or voltage resistance over time. Fortunately, most objects produce a unique signature based on a number of features embedded within the signature. The objective of this presentation is to demonstrate and compare the usefulness of a variety of Neural Networks on object signature discrimination as well as to present the limitations and common problems associated with this task. The networks used are back propagation, recurrent back propagation, Kohonen, and the Padaline, each of which are well known networks and are easily obtained. Issues such as dynamic and cluttered environments are discussed with possible solutions, including pre/post filtering, fuzzy logic, and multiple networks. In support of the discussion, the preparation and results of applying each type of network to the radiometric signature of simple objects such as cubes, spheres, and cones are presented, thus demonstrating a variety of input, output, and pre/post- processing techniques.

Paper Details

Date Published: 16 September 1992
PDF: 9 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139997
Show Author Affiliations
James R. Dyvig, Photon Research Associates, Inc. (United States)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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