Image thresholding based on the EM algorithm and the generalized Gaussian distribution

@article{Bazi2007ImageTB,
  title={Image thresholding based on the EM algorithm and the generalized Gaussian distribution},
  author={Yakoub Bazi and Lorenzo Bruzzone and Farid Melgani},
  journal={Pattern Recognition},
  year={2007},
  volume={40},
  pages={619-634}
}
In this paper, a novel parametric and global image histogram thresholding method is presented. It is based on the estimation of the statistical parameters of “object” and “background” classes by the expectation–maximization (EM) algorithm, under the assumption that these two classes follow a generalized Gaussian (GG) distribution. The adoption of such a statistical model as an alternative to the more common Gaussian model is motivated by its attractive capability to approximate a broad variety… CONTINUE READING
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Discriminant analysis by Gaussian mixtures

  • T. Hastie, R. Tibshirani
  • J. R. Stat. Soc. B 58 (1996) 155–176. 634 Y…
  • 2007
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