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Proceedings Article

Sparsity inspired automatic target recognition

[+] Author Affiliations
Vishal M. Patel, Rama Chellappa

Univ. of Maryland, College Park (USA)

Nasser M. Nasrabadi

U.S. Army Research Lab. (USA)

Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960Q (May 12, 2010); doi:10.1117/12.850533
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From Conference Volume 7696

  • Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
  • Firooz A. Sadjadi; Abhijit Mahalanobis; Steven L. Chodos; William E. Thompson; David P. Casasent; Tien-Hsin Chao
  • Orlando, Florida | April 05, 2010

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.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Vishal M. Patel ; Nasser M. Nasrabadi and Rama Chellappa
"Sparsity inspired automatic target recognition", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960Q (May 12, 2010); doi:10.1117/12.850533; http://dx.doi.org/10.1117/12.850533


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