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Proceedings Paper

Correlation filters that generalize well
Author(s): Rajesh Shenoy; David P. Casasent
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Paper Abstract

Distortion-invariant correlation filters are used to detect and recognize distorted objects in image scenes. We describe a new way to design distortion-invariant correlation filters that ensures good generalization (same performance on training and test sets). The traditional way of designing correlation filters uses different types of frequency domain preprocessing and linear combination of training images. We show that these different approaches can be implemented in a framework using linear combination of eigen-images of preprocessed training data. Using eigen-domain data is shown to generalize well regardless of preprocessing used. We show results on SAR data using eigen-MINACE filters.

Paper Details

Date Published: 23 March 1998
PDF: 11 pages
Proc. SPIE 3386, Optical Pattern Recognition IX, (23 March 1998); doi: 10.1117/12.304754
Show Author Affiliations
Rajesh Shenoy, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 3386:
Optical Pattern Recognition IX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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