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Proceedings Paper

Dynamic tree segmentation of sonar imagery
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Paper Abstract

High-resolution sonar images of the sea floor contain rich spatial information that varies widely depending on survey location, sea state, and sensor platform-induced artifacts. Automatically segmenting sonar images into labeled regions can have several useful applications such as creating high-resolution bottom maps and adapting automatic target recognition schemes to perform optimally given the measured environment. This paper presents a method for sonar image segmentation using graphical models known as dynamic trees (DTs). A DT is a mixture of simply-connected tree-structured Bayesian networks (TSBNs), a hierarchical two-dimensional Bayesian network, where the leaf node states of each TSBN are the label of each image pixel. The DT segmentation task is to find the best TSBN mixture that represents the underlying data. A novel use of the K-distribution as a likelihood function for associating sonar image pixels with the appropriate bottom-type label is introduced. A simulated annealing stochastic search method is used to determine the maximum a posteriori (MAP) DT quadtree structure for each sonar image. Segmentation results from several images are presented and discussed.

Paper Details

Date Published: 26 April 2007
PDF: 12 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530P (26 April 2007); doi: 10.1117/12.720290
Show Author Affiliations
J. Tory Cobb, Naval Surface Warfare Ctr. (United States)
K. Clint Slatton, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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