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24 December 2013Variational Bayesian level set for image segmentation
In this paper, we present a variational Bayesian framework for level set image segmentation, which utilizes Gaussian mixtures model to approximate the posteriors of image intensities inside and outside of the zero level set, respectively. The active curve will evolve according to the approximate log marginal probability of each region and a partition of image is obtained by the sign of the level set function. Our method provides a flexible probabilistic framework to model image data with flexible Gaussian mixtures model. Experimental results demonstrate our approach is comparable to classical level set segmentation method.
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Han-Bing Qu, Lin Xiang, Jia-Qiang Wang, Bin Li, Hai-Jun Tao, "Variational Bayesian level set for image segmentation," Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670D (24 December 2013); https://doi.org/10.1117/12.2049814