Sonar image segmentation based on GMRF and level-set models |
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Authors: | Xiu-Fen Ye Zhe-Hui Zhang Hong-Ling Guan |
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Institution: | a College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, PR China b Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada K1S 5B6 |
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Abstract: | 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. |
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Keywords: | Sonar image GMRF Level set Segmentation |
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