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Proceedings Paper

Sparsity inspired automatic target recognition
Author(s): Vishal M. Patel; Nasser M. Nasrabadi; Rama Chellappa
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Paper Abstract

In this paper, we develop a framework for using only the needed data for automatic target recognition (ATR) algorithms using the recently developed theory of sparse representations and compressive sensing (CS). We show how sparsity can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm in terms of the recognition rate on the well known Comanche forward-looking infrared (FLIR) data set consisting of ten different military targets at different orientations.

Paper Details

Date Published: 13 May 2010
PDF: 8 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960Q (13 May 2010); doi: 10.1117/12.850533
Show Author Affiliations
Vishal M. Patel, Univ. of Maryland, College Park (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Rama Chellappa, Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)

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