Overlapping image segmentation for context-dependent anomaly detection

James Theiler; Lakshman Prasad

doi:10.1117/12.883326

20 May 2011 Overlapping image segmentation for context-dependent anomaly detection

James Theiler, Lakshman Prasad

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 804807 (2011) https://doi.org/10.1117/12.883326
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

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.

Citation Download Citation

James Theiler and 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); https://doi.org/10.1117/12.883326

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