
Proceedings Paper
Discriminative dictionaries for automated target recognitionFormat | Member Price | Non-Member Price |
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Paper Abstract
We present an approach for discriminating among dierent classes of imagery in a scene. Our intended application
is the detection of small watercraft in a littoral environment where both targets and land- and sea-based clutter
are present. The approach works by training dierent overcomplete dictionaries to model the dierent image
classes. The likelihood ratio obtained by applying each model to the unknown image is then used as the
discriminating test statistic. We rst demonstrate the approach on an illustrative test problem and then apply
the algorithm to short-wave infrared imagery with known targets.
Paper Details
Date Published: 17 May 2012
PDF: 7 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920N (17 May 2012); doi: 10.1117/12.920725
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
PDF: 7 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920N (17 May 2012); doi: 10.1117/12.920725
Show Author Affiliations
C. C. Olson, Sotera Defense Solutions, Inc. (United States)
K. P. Judd, U.S. Naval Research Lab. (United States)
K. P. Judd, U.S. Naval Research Lab. (United States)
L. N. Smith, U.S. Naval Research Lab. (United States)
J. M. Nichols, U.S. Naval Research Lab. (United States)
J. M. Nichols, U.S. Naval Research Lab. (United States)
Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)
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