Simulated Annealing in Compound Gaussian Random Fields

IEEE Transactions on Information Theory

Volumn 36, Issue 1, 1990, Pages 94-107

  Woods, John W ^b  

^a TELCORDIA TECHNOLOGIES   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; IMAGE PROCESSING; PROBABILITY--RANDOM PROCESSES;

GAUSS-MARKOV RANDOM FIELDS; GAUSSIAN RANDOM FIELDS; SIMULATED ANNEALING; STOCHASTIC RELAXATION;

OPTIMIZATION;

EID: 0025229399     PISSN: 00189448     EISSN: 15579654     Source Type: Journal    
DOI: 10.1109/18.50377     Document Type: Article

References (18)

1. R. L. Dobrushin, “Central limit theorems for nonstationary Markov chains II,” Theory Probab. Appl., vol. 1, pp. 329–383, 1954 (English translation).
2. J. L. Doob, Stochastic Processes. New York: Wiley, 1953, pp. 170–291.
3. S. Genian and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 721–741, Nov. 1984.
4. B. Gidas, “Nonstationary Markov chains and convergence of the annealing algorithm,” J. Statist. Phys., vol. 39, pp. 73–131, 1985.
5. J. M. Hammersley and D. C. Handscomb, Monte Carlo Methods. London: Methuen, 1965, pp. 113–126.
6. F. C. Jeng, “Compound Gauss-Markov random fields for image estimation and restoration,” Ph. D. dissertation, Rensselaer Poly-tech. Inst., Troy, NY, June, 1988.
7. R. Kashyap and R. Chellappa, “Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Inform. Theory, vol. IT-29, pp. 60–72, Jan. 1983.
8. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680, May 1983.
9. E. Kreyszig, Introductory Functional Analysis with Applications. New York: Wiley, 1978, pp. 102–103.
10. A. Lasota and M. C. Mackey, Probabilistic Properties on Deterministic Systems. New York: Cambridge Univ. Press, 1985.
11. R. W. Madsen and D. L. Isaacson, “Strongly ergodic behavior for nonstationary Markov processes,” Ann. Probab., vol. 1, No. 2, pp. 329–335, 1973.
12. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys., vol. 21, pp. 1087–1092, 1953.
13. D. Mitra, F. Romeo, and A. Sangiovanni-Vincentelli, “Convergence and finite-time behavior of simulated annealing,” Adv. Appl. Probab., vol. 18, pp. 747–771, 1986.
14. D. W. Murray, A. Kashko, and H. Buxton, “A parallel approach to the picture restoration algorithm of Geman and Geman on an SIMD machine,” Image Vision Comput., vol. 4, pp. 133–142, Aug. 1986.
15. M. B. Nevelson and R. Z. Hasminskii, Stochastic Approximation and Recursive Estimation (translations of Mathematical Mono-graphs, vol. 47) Providence, RI: Amer. Math. Soc., 1973.
16. J. W. Woods, S. Dravida, and R. Mediavilla, “Image estimation using doubly stochastic Gaussian random field models,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, pp. 245–253, Mar. 1987.
17. J. W. Woods, “Two dimensional discrete Markovian fields,” IEEE Trans. Inform. Theory, vol. IT-18, pp. 232–240, Mar. 1972.
18. J. M. Wozencraft and I. M. Jacobs, Principles of Communication Engineering. New York: Wiley, 1965, pp. 82.