Variational Bayesian level set for image segmentation

Han-Bing Qu; Lin Xiang; Jia-Qiang Wang; Bin Li; Hai-Jun Tao

doi:10.1117/12.2049814

24 December 2013 Variational Bayesian level set for image segmentation

Han-Bing Qu, Lin Xiang, Jia-Qiang Wang, Bin Li, Hai-Jun Tao

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

Abstract

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.

Citation Download Citation

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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