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Proceedings Paper

Overlapping image segmentation for context-dependent anomaly detection
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Paper Abstract

The challenge of finding small targets in big images lies in the characterization of the background clutter. The more homogeneous the background, the more distinguishable a typical target will be from its background. One way to homogenize the background is to segment the image into distinct regions, each of which is individually homogeneous, and then to treat each region separately. In this paper we will report on experiments in which the target is unspecified (it is an anomaly), and various segmentation strategies are employed, including an adaptive hierarchical tree-based scheme. We find that segmentations that employ overlap achieve better performance in the low false alarm rate regime.

Paper Details

Date Published: 20 May 2011
PDF: 11 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804807 (20 May 2011); doi: 10.1117/12.883326
Show Author Affiliations
James Theiler, Los Alamos National Lab. (United States)
Lakshman Prasad, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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