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Proceedings Paper

Detection system performance metrics with scene content dependencies
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Paper Abstract

Traditional detection system performance metrics, such a probability of detection and probability of false alarm, depend only on how the system responds to individual target-sized regions-of-interest (ROIs). The composition of the larger scene does not affect those metrics. There are circumstances however, where a user of a detection system wants to know, "For a given cue, what is the probability that the cue is correct?" or perhaps the detector is being used to determine a property of the overall scene. As an example of the latter case, suppose the detection system is looking for diseased cells in a tissue sample. Even if only one diseased cell exists, the whole "scene" represents a diseased individual. In both cases, the user-perspective or the scene-based perspective, the natural performance metrics depend on the scene content, especially the numbers of target and confuser ROIs. This paper defines scene-content dependent (SCD) performance metrics for detection systems, develops a theory for computing them, and illustrates properties of the metrics with examples. The SCD performance theory enabled determination of the example metrics in about two hours of computation; whereas Monte Carlo methods would have taken almost a year and direct testing would have been almost impossible.

Paper Details

Date Published: 19 May 2005
PDF: 19 pages
Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); doi: 10.1117/12.609758
Show Author Affiliations
Timothy D. Ross, Air Force Research Lab. (United States)
William E. Pierson, Air Force Research Lab. (United States)
Edmund G. Zelnio, Air Force Research Lab. (United States)
Kevin L. Priddy, Jacobs Engineering Sverdrup Technology (United States)


Published in SPIE Proceedings Vol. 5808:
Algorithms for Synthetic Aperture Radar Imagery XII
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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