Correlation filters that generalize well

Rajesh Shenoy; David P. Casasent

doi:10.1117/12.304754

23 March 1998 Correlation filters that generalize well

Rajesh Shenoy, David P. Casasent

Proceedings Volume 3386, Optical Pattern Recognition IX; (1998) https://doi.org/10.1117/12.304754
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Rajesh Shenoy and David P. Casasent "Correlation filters that generalize well", Proc. SPIE 3386, Optical Pattern Recognition IX, (23 March 1998); https://doi.org/10.1117/12.304754

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