@inproceedings{44e6e0cdd0184c5297e5a95d8d1251b3,
title = "Sparsity inspired automatic target recognition",
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.",
keywords = "Automatic target recognition, Compressed sensing, Forward-looking infrared (FLIR) imagery, Sparse representation",
author = "Patel, {Vishal M.} and Nasrabadi, {Nasser M.} and Rama Chellappa",
year = "2010",
doi = "10.1117/12.850533",
language = "English (US)",
isbn = "9780819481603",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI",
note = "Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI ; Conference date: 05-04-2010 Through 08-04-2010",
}