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Proceedings Paper

A new SVM for scale, aspect, and depression angle tolerant IR object recognition
Author(s): Yu-Chiang Frank Wang; David Casasent
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Paper Abstract

In most ATR applications, objects are not only present with thermal and aspect view angle variations, its size (range) also changes as the sensor approaches the target, and depression angle variations can exist. Therefore, it is important and realistic to know how to handle these variations. We apply our new SVRDM (support vector representation and discrimination machine) classifier to address these problems. The SVRDM classifier has good generalization (like the standard SVM does), and it has the added property of a good rejection ability. In other words, it not only gives very promising recognition results on the true target classes, it is also able to reject other unseen objects (referred to as confusers). We address the following variation issues: the scale range one SVRDM can recognize when trained on data at one or more ranges, the depression angle difference one SVRDM can recognize when trained on data at only one (or several) depression angles, and the number of aspect views needed to be included in the training set to handle recognition of targets with aspect variations, and the classification and rejection performance. Thus, our results are most unique and worthwhile but are not easily compared to prior work. Recognition and rejection test results are presented on both simulated and real infra-red (IR) data.

Paper Details

Date Published: 9 April 2007
PDF: 12 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 657408 (9 April 2007); doi: 10.1117/12.715192
Show Author Affiliations
Yu-Chiang Frank Wang, Carnegie Mellon Univ. (United States)
David Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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