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Proceedings Paper

Flexible histograms: a multiresolution target discrimination model
Author(s): Jeremy S. De Bonet; Paul Viola; John W. Fisher III
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Paper Abstract

In previous work we have developed a methodology for texture recognition and synthesis that estimates and exploits the dependencies across scale that occur within images. In this paper we discuss the application of this technique to synthetic aperture radar (SAR) vehicle classification. Our approach measures characteristic cross-scale dependencies in training imagery; targets are recognized when these characteristic dependencies are detected. We present classification results over a large public database containing SAR images of vehicles. Classification performance is compared to the Wright Patterson baseline classifier. These preliminary experiments indicate that this approach has sufficient discrimination power to perform target detection/classification in SAR.

Paper Details

Date Published: 18 September 1998
PDF: 12 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323870
Show Author Affiliations
Jeremy S. De Bonet, MIT Artificial Intelligence Lab. (United States)
Paul Viola, MIT Artificial Intelligence Lab. (United States)
John W. Fisher III, MIT Artificial Intelligence Lab. (United States)

Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)

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