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Proceedings Paper

Markov random-field-based anomaly screening algorithm
Author(s): Martin G. Bello
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Paper Abstract

A novel anomaly screening algorithm is described which makes use of a regression diagnostic associated with the fitting of Markov Random Field (MRF) models. This regression diagnostic quantifies the extent to which a given neighborhood of pixels is atypical, relative to local background characteristics. The screening algorithm consists first in the calculation of an MRF-based anomoly statistic values. Next, 'blob' features, such as pixel count and maximal pixel intensity are calculated, and ranked over the image, in order to 'filter' the blobs to some final subset of most likely candidates. Receiver operating characteristics obtained from applying the above described screening algorithm to the detection of minelike targets in high- and low-frequency side-scan sonar imagery are presented together with results obtained from other screening algorithms for comparison, demonstrating performance comparable to trained human operators. In addition, real-time implementation considerations associated with each algorithmic component of the described procedure are identified.

Paper Details

Date Published: 20 June 1995
PDF: 9 pages
Proc. SPIE 2496, Detection Technologies for Mines and Minelike Targets, (20 June 1995); doi: 10.1117/12.211344
Show Author Affiliations
Martin G. Bello, Charles Stark Draper Lab. Inc. (United States)


Published in SPIE Proceedings Vol. 2496:
Detection Technologies for Mines and Minelike Targets
Abinash C. Dubey; Ivan Cindrich; James M. Ralston; Kelly A. Rigano, Editor(s)

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