Multi-image texton selection for sonar image seabed co-segmentation

J. Tory Cobb; Alina Zare

doi:10.1117/12.2016427

7 June 2013 Multi-image texton selection for sonar image seabed co-segmentation

J. Tory Cobb, Alina Zare

Proceedings Volume 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII; 87090H (2013) https://doi.org/10.1117/12.2016427
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

J. Tory Cobb and Alina Zare "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 (7 June 2013); https://doi.org/10.1117/12.2016427

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