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Learning the parameters of a hidden Markov random field image model: A simple example
Source Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications table of contents
Spa-Balmoral, Belgium
Pages: 141 - 163  
Year of Publication: 1987
ISBN:0-387-17700-0
Authors
Pierre Devijver  Philips Research Laboratory, Brussels, Belgium
Michel M. Dekesel  Philips Research Laboratory, Brussels, Belgium
Publisher
Springer-Verlag  London, UK
Bibliometrics
Downloads (6 Weeks): n/a,   Downloads (12 Months): n/a,   Citation Count: 3
Additional Information:

cited by   index terms   collaborative colleagues  

Tools and Actions: Review this Article  

CITED BY  3
D. Geman , S. Geman , C. Graffigne , P. Dong, Boundary Detection by Constrained Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.7, p.609-628, July 1990
Chi-hsin Wu , Peter C. Doerschuk, Tree Approximations to Markov Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.4, p.391-402, April 1995
Chi-hsin Wu , Peter C. Doerschuk, Cluster Expansions for the Deterministic Computation of Bayesian Estimators Based on Markov Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.3, p.275-293, March 1995

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION

Additional Classification:
  F. Theory of Computation
  F.1 COMPUTATION BY ABSTRACT DEVICES
      F.1.2 Modes of Computation
          Subjects: Probabilistic computation

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms


General Terms:
Algorithms, Design, Experimentation, Performance, Theory

Collaborative Colleagues:
Pierre Devijver: colleagues
Michel M. Dekesel: colleagues

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