Use of the mean-field approximation in an em-based approach to unsupervised stochastic model-based image segmentation

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 3, Issue , 1992, Pages 57-60

  Langan, David A ^a   Molnar, Karl J ^a   Modestino, James W ^b   Zhang, Jun ^c  

^a GE GLOBAL RESEARCH   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^c UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO SIGNAL PROCESSING; MARKOV PROCESSES; SIGNAL PROCESSING; SPEECH COMMUNICATION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

COMPUTATIONAL ADVANTAGES; CONVERGENCE CRITERION; ENERGY FUNCTIONS; MARGINAL DISTRIBUTION; MARKOV RANDOM FIELDS; MEAN FIELD APPROXIMATION; STATE MODELING; UNSUPERVISED IMAGE SEGMENTATION;

IMAGE SEGMENTATION;

EID: 84997702290     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.1992.226277     Document Type: Conference Paper

References (13)

1. J. Modestino, R. Fries, and A. Vickers, "Texture discrimination based upon an assumed stochastic texture model", IEEE Trans. PAMI, 3(3):557-580, September 1981.
2. H. Cline, W. Lorensen, R. Kikinis, and F. Jolesz, "Three-Dimensional Segmentation of MR Images of the Head Using Probability and Connectivity", Journal of Computer Assisted Tomography, 14(6):1037-1045, 1990.
3. D. Curran, Principles of Remote Sensing, Longman, New York, New York, 1985.
4. C. Therrien, T. Quatieri, and D. Dudgeon, "Statistical Model-Based Algorithms for Image Analysis", Proc. of the IEEE, 74(4):532-551, April 1986.
5. D. Titterington, A. Smith, and U. Makov, Statistical Analysis of Finite Mixture Distributions, Wiley, New York, 1985.
6. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images", IEEE Trans. PAMI, 6(6):721-741, Nov. 1984.
7. H. Derin and H. Elliott, "Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields", IEEE Trans. PAMI, 9(1):39-55, January 1987.
8. P. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm", J. Roy. Stat., 39:1-38, 1977.
9. J. Zhang, J. Modestino, and D. Langan, "Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation", submitted to IEEE Trans. Sig. Proc., July 1991.
10. D. Chandler, Introduction to Modern Statistical Mechanics, Oxford University Press, New York, New York, 1987.
11. J. Zhang and J. Modestino, "The Mean Field Theory in EM Procedures for Markov Random Fields", In Proc. 1991 Int. Symp. Info. Theory, Budapest, June 1991.
12. J. Besag, "On the Statistical Analysis of Dirty Pictures", J. Roy. Stat., Series B, 48(3):259-302, 1986.
13. R. Blahut, Principles and Practice of Information Theory, Addison Wesley, 1987.