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Proceedings Paper

Improved MINACE infrared detection filters
Author(s): David Casasent; Songyot Nakariyakul
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Paper Abstract

Detection is one of the most formidable problems in automatic target recognition, since it involves locating multiple classes of targets of interest with distortions present in cluttered scenes. Fast and efficient algorithms are needed for detection, since in detection we need to analyze every local region of large image scenes. Minimum noise and correlation energy (MINACE) filters are attractive distortion-invariant filters (DIFs); we consider MINACE filter use in detection, since they provide sharp correlation peak values for targets and overcome the effect of aspect view distortions in the input data. Most prior work on DIFs considered classification, not detection. MINACE filters seem to require fewer filters than do other DIFs, and they recognize objects with aspect views different by 15° from those present in the training set. They are also shift-invariant and require only a few filters to handle detection of multiple target classes. We test our improved MINACE filters to detect 8 classes of objects in an infrared (IR) database with a ±90° range of aspect views. Initial test results are excellent with only 3 filters needed and very low false alarm rates.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5816, Optical Pattern Recognition XVI, (28 March 2005); doi: 10.1117/12.607980
Show Author Affiliations
David Casasent, Carnegie Mellon Univ. (United States)
Songyot Nakariyakul, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 5816:
Optical Pattern Recognition XVI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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