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Proceedings Paper

Automatic multi-target recognition from two classes using quadratic correlation filters
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Paper Abstract

Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic target recognition (ATR) applications. Quadratic CFs (QCFs) can improve performance over linear CFs. QCFs are able to detect one class of targets and reject clutter. We present a method to increase the QCF capabilities to detect two classes of targets and reject clutter. We integrate the ATR tasks of detection, recognition, and tracking algorithms using the Multi-Frame Correlation Filter (MFCF) framework. Our simulation results demonstrate the algorithm's ability to detect multiple targets from two classes while rejecting clutter.

Paper Details

Date Published: 13 May 2010
PDF: 11 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 76960C (13 May 2010); doi: 10.1117/12.852716
Show Author Affiliations
Andres Rodriguez, Carnegie Mellon Univ. (United States)
B.V. K. Vijaya Kumar, Carnegie Mellon Univ. (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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