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Proceedings Paper

Modeling synthetic infrared data for classifier development
Author(s): Bruce A. Weber; Joseph A. Penn
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Paper Abstract

In an effort to improve the usefulness of computer classifiers for military applications, the U.S. Army Research Laboratory has begun to develop a database of synthetic infrared target chips. Once created, this database will aid in the training and testing of both human and computer classifiers, and will provide a way to train classifiers on targets and clutter environments with little real data available. Results presented below will show that classifier performance trained on synthetic data is improving but is, in general, poorer than when trained on real data, that individual synthetic target models perform much better than other models, providing evidence that better overall performance may yet be achievable, that synthetic data thus far created is highly self-similar and/or to some unknown extent represents real data not included in our database, and that enhanced performance of classifiers trained on small amounts of real data can be achieved by adding limited amounts of synthetic data.

Paper Details

Date Published: 17 August 2000
PDF: 9 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395562
Show Author Affiliations
Bruce A. Weber, Army Research Lab. (United States)
Joseph A. Penn, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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