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Chung, G. and Vese, L. (2005) Energy Minimization Based Segmentation and Denoising Using a Multilayer Level Set Approach. Energy Minimization Methods in Computer Vision and Pattern Recognition, 3757, 439-455.
http://dx.doi.org/10.1007/11585978_29

has been cited by the following article:

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

  AUTHORS: Celia A. Z. Barcelos, Yunmei Chen, Fuhua Chen

  KEYWORDS: Image Segmentation, Variational Model, Gaussian Mixture

  JOURNAL NAME: Applied Mathematics, Vol.5 No.18, October 30, 2014

  ABSTRACT: 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.