20 May 2011 Overlapping image segmentation for context-dependent anomaly detection
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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.
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James Theiler, James Theiler, Lakshman Prasad, Lakshman Prasad, } "Overlapping image segmentation for context-dependent anomaly detection", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 804807 (20 May 2011); doi: 10.1117/12.883326; https://doi.org/10.1117/12.883326

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