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Proceedings Paper

Multi-image texton selection for sonar image seabed co-segmentation
Author(s): J. Tory Cobb; Alina Zare
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Paper Abstract

In this paper we describe an unsupervised approach to seabed co-segmentation over the multiple sonar images collected in sonar surveys. We adapt a traditional single image segmentation texton-based approach to the sonar survey task by modifying the texture extraction filter bank to better model possible sonar image textures. Two different algorithms for building a universal texton library are presented that produce common pixel labels across multiple images. Following pixel labeling with the universal texton library, images are quantized into superpixels and co-segmented using a DP clustering algorithm. The segmentation results for both texton library selection criteria are contrasted and compared for a labeled set of SAS images with various discernable textures.

Paper Details

Date Published: 7 June 2013
PDF: 11 pages
Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87090H (7 June 2013); doi: 10.1117/12.2016427
Show Author Affiliations
J. Tory Cobb, Naval Surface Warfare Ctr., Panama City Div. (United States)
Alina Zare, Univ. of Missouri-Columbia (United States)


Published in SPIE Proceedings Vol. 8709:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII
J. Thomas Broach; Jason C. Isaacs, Editor(s)

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