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Proceedings Paper

Symmetrized local co-registration optimization for anomalous change detection
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Paper Abstract

The goal of anomalous change detection (ACD) is to identify what unusual changes have occurred in a scene, based on two images of the scene taken at different times and under different conditions. The actual anomalous changes need to be distinguished from the incidental differences that occur throughout the imagery, and one of the most common and confounding of these incidental differences is due to the misregistration of the images, due to limitations of the registration pre-processing applied to the image pair. We propose a general method to compensate for residual misregistration in any ACD algorithm which constructs an estimate of the degree of "anomalousness" for every pixel in the image pair. The method computes a modified misregistration-insensitive anomalousness by making local re-registration adjustments to minimize the local anomalousness. In this paper we describe a symmetrized version of our initial algorithm, and find significant performance improvements in the anomalous change detection ROC curves for a number of real and synthetic data sets.

Paper Details

Date Published: 27 January 2010
PDF: 10 pages
Proc. SPIE 7533, Computational Imaging VIII, 753307 (27 January 2010); doi: 10.1117/12.845210
Show Author Affiliations
Brendt Wohlberg, Los Alamos National Lab. (United States)
James Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 7533:
Computational Imaging VIII
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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