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7 June 2013 Multi-image texton selection for sonar image seabed co-segmentation
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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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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