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Proceedings Paper

Variational Bayesian level set for image segmentation
Author(s): Han-Bing Qu; Lin Xiang; Jia-Qiang Wang; Bin Li; Hai-Jun Tao
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Paper 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.

Paper Details

Date Published: 24 December 2013
PDF: 5 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670D (24 December 2013); doi: 10.1117/12.2049814
Show Author Affiliations
Han-Bing Qu, Beijing Academy of Science and Technology (China)
Lin Xiang, China Jiliang Univ. (China)
Jia-Qiang Wang, Beijing Academy of Science and Technology (China)
Bin Li, Beijing Academy of Science and Technology (China)
Hai-Jun Tao, China Jiliang Univ. (China)


Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Jianhong Zhou; Antanas Verikas, Editor(s)

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