Soft Image Segmentation Based on the Mixture of Gaussians and the Phase-Transition Theory


In this paper, we propose a new soft multi-phase segmentation model where it is assumed that the pixel intensities are distributed as a Gaussian mixture. The model is formulated as a minimization problem through the use of the maximum likelihood estimator and phase-transition theory. The mixture coefficients, which are estimated using a spatially varying mean and variance procedure, are used for image segmentation. The experimental results indicate the effectiveness of the method.

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Barcelos, C. , Chen, Y. and Chen, F. (2014) Soft Image Segmentation Based on the Mixture of Gaussians and the Phase-Transition Theory. Applied Mathematics, 5, 2888-2898. doi: 10.4236/am.2014.518275.

Conflicts of Interest

The authors declare no conflicts of interest.


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