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.

Links:

Citation:

J. T. Cobb and A. Zare, “Multi-Image texton selection for sonar image seabed co-segmentation,” in Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013. 

@InProceedings{cobb2013multi,
Title = {Multi-Image texton selection for sonar image seabed co-segmentation},
Author = {Cobb, James T. and Zare, Alina},
Booktitle = {Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII},
Year = {2013},
Month = {June},
Number = {87090H},
Volume = {8709},
Doi = {10.1117/12.2016427},
}