Image segmentation and parameters estimation based on fuzzy Markov random field with possibility theory

[in Chinese]; [in Chinese]; [in Chinese]

Journals >Infrared and Laser Engineering >Volume 36 >Issue 5 >Page 733 > Article

Infrared and Laser Engineering
Vol. 36, Issue 5, 733 (2007)

Image segmentation and parameters estimation based on fuzzy Markov random field with possibility theory

[in Chinese]^*, [in Chinese], and [in Chinese]

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[in Chinese]

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[in Chinese], [in Chinese], [in Chinese]. Image segmentation and parameters estimation based on fuzzy Markov random field with possibility theory[J]. Infrared and Laser Engineering, 2007, 36(5): 733 Copy Citation Text

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References

[1] SALZENSTEIN F,PIECZYNSKI W.Parameter estimation in hidden fuzzy Markov random fields and image segmentation[J].Graphical Models and Image Processing,1997,59(4):205-231.

[2] RUAN S,BLOYET D,REVENU M.Cerebral magnetic resonance image segmentation using fuzzy Markov random fields[J].IEEE Trans on Medical Imaging,2002,16(2):237-240.

[5] GEMAN S,GEMAN D.Stochastic relaxation,Gibbs distributions and Bayesian restoration of images[J].IEEE Trans PAMI,1984,6 (6):721-741.

[6] CHAN M,LEVITAN E,HERMAN G T.Image-modeling gibbs distribution for bayesian restoration[J].IEEE Trans on Image Processing,1994,12(10):1259-1272.

[7] GU D B,SUN J X.EM image segmentation algorithm based on an inhomogeneous hidden MRF model[J].IEE Proc-Vis Image Signal Process,2005,152(2):184-190.

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[in Chinese], [in Chinese], [in Chinese]. Image segmentation and parameters estimation based on fuzzy Markov random field with possibility theory[J]. Infrared and Laser Engineering, 2007, 36(5): 733

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