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Proceedings Paper

Spatial stochastic models for seabed object detection
Author(s): B. R. Calder; L. M. Linnett; D. R. Carmichael
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Paper Abstract

We introduce two statistical models designed to detect discrete objects in sidescan SONAR which consider complimentary approaches to the problem. The first considers a complex textural model for the objects and implements detection through a dual hypothesis on texture class presence, while the second implements a complex Gibbs field model of the image and utilizes prior knowledge of typical object morphologies to support its detection rate. The models are demonstrated on examples of different seabed sediments and object types, and are shown to be reliable in operation. The common theme of the two models is use of spatial context in analysis, which, we argue, is a very powerful technique for improving the flexibility and reliability of any analysis system.

Paper Details

Date Published: 22 July 1997
PDF: 11 pages
Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280913
Show Author Affiliations
B. R. Calder, Heriot-Watt Univ. (United Kingdom)
L. M. Linnett, Heriot-Watt Univ. (United Kingdom)
D. R. Carmichael, Defense Evaluation and Research Bincleaves (United Kingdom)

Published in SPIE Proceedings Vol. 3079:
Detection and Remediation Technologies for Mines and Minelike Targets II
Abinash C. Dubey; Robert L. Barnard, Editor(s)

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