Abstract
The study is to investigate a new representation of a partition of an image domain into a number of regions using level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing two terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all time during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results.
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Acknowledgments:
The research described in this paper was funded by National international technology cooperation plan (2007DFA20790), Jiangxi province scientific and technological achievements promotion plan(GanCaiJiao[2011]243), Jiangxi Province Key Lab for Digital Land (DLLJ201301), Science & technology Project of Jiangxi Province Education Department (GJJ13446).
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Fang, J., Liu, H., Deng, J., Gong, Y., Xu, H., Liu, J. (2014). Multiphase Image Segmentation from a Statistical Framework. In: Farag, A., Yang, J., Jiao, F. (eds) Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013). Lecture Notes in Electrical Engineering, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41407-7_37
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DOI: https://doi.org/10.1007/978-3-642-41407-7_37
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