24 December 2013 Variational Bayesian level set for image segmentation
Author Affiliations +
Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90670D (2013) https://doi.org/10.1117/12.2049814
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Han-Bing Qu, Han-Bing Qu, Lin Xiang, Lin Xiang, Jia-Qiang Wang, Jia-Qiang Wang, Bin Li, Bin Li, Hai-Jun Tao, 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); doi: 10.1117/12.2049814; https://doi.org/10.1117/12.2049814


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