Sonar image segmentation based on GMRF and level-set models

We propose two new level-set models to address the segmentation problem in sonar images. Local texture features, extracted using the Gauss–Markov random field model, are integrated into level-set energy functions to dynamically select regions of interest. Then, new two-phase level-set and multiphase...

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Bibliographic Details
Published in:Ocean engineering 2010-07, Vol.37 (10), p.891-901
Main Authors: Ye, Xiu-Fen, Zhang, Zhe-Hui, Liu, Peter X., Guan, Hong-Ling
Format: Article
Language:English
Subjects:
Earth, ocean, space
Energy use
Exact sciences and technology
External geophysics
GMRF
Image segmentation
Level set
Marine
Marine optics and underwater sound
Mathematical models
Ocean engineering
Physics of the oceans
Segmentation
Sonar
Sonar image
Surface layer
Texture
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Summary:We propose two new level-set models to address the segmentation problem in sonar images. Local texture features, extracted using the Gauss–Markov random field model, are integrated into level-set energy functions to dynamically select regions of interest. Then, new two-phase level-set and multiphase level-set models are obtained by minimizing each new energy function, and the selection of model parameters is analyzed. The proposed models do not require re-initialization, which is usually a very costly procedure. Segmentation experiments on both synthetic and real sonar images show that the proposed two level-set models are accurate and robust when they are applied to noisy sonar images.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2010.03.003