Multi-Image Texton Selection for Sonar Image Seabed Co-Segmentation

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.

 

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Plain Text:

Cobb, J. T.; Zare, A., “Multi-Image Texton Selection for Sonar Image Seabed Co-Segmentation,” Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 87090H(June 7, 2013).

Bibtex:

@INPROCEEDINGS{CobbZare:2013a,
author={Cobb, J. T. and Zare, A.},
journal={Proc. SPIE 8709 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII},
title={Multi-Image Texton Selection for Sonar Image Seabed Co-Segmentation},
year={2013}, month={June},
volume={8709},
number={87090H},
}

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