Analysis of stochastic automata algorithm for relaxation labelling

A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will always converge to a consistent labeling.< >

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Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 16; no. 5; pp. 538 - 543
Main Authors: Sastry, P.S., Thathachar, M.A.L.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.1994
IEEE Computer Society
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ISSN:0162-8828
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Abstract A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will always converge to a consistent labeling.< >
AbstractList A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will always converge to a consistent labeling.< >
A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will always converge to a consistent labeling
A parallel stochastic algorithm for relaxation labelling is analyzed. For the case of symmetric compatibility functions, it is proved that the algorithm will always converge to a consistent labeling.
Author Sastry, P.S.
Thathachar, M.A.L.
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Cites_doi 10.1016/0004-3702(81)90026-6
10.1109/TPAMI.1983.4767390
10.1109/TPAMI.1981.4767069
10.1007/BF02811324
10.1109/TPAMI.1986.4767779
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Issue 5
Keywords Learning
Relaxation
Computer vision
Parallel algorithm
Learning automata
Constraint satisfaction
Stochastic algorithm
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References jianhua (ref6) 1988
narendra (ref8) 1989
ref9
ref4
kushner (ref10) 1984
narendra (ref11) 1989
ref3
banerjee (ref5) 1989
basar (ref7) 1982
ref2
ref1
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  doi: 10.1016/0004-3702(81)90026-6
– ident: ref3
  doi: 10.1109/TPAMI.1983.4767390
– year: 1982
  ident: ref7
  publication-title: Dynamic Noncooperative Game Theory
– year: 1989
  ident: ref5
  publication-title: Stochastic Relaxation Paradigms for Low Level Vision
– year: 1989
  ident: ref11
  publication-title: Stable Adaptive Systems
– ident: ref4
  doi: 10.1109/TPAMI.1981.4767069
– year: 1984
  ident: ref10
  publication-title: Approximation and Weak Convergence Methods for Random Processes
– ident: ref9
  doi: 10.1007/BF02811324
– year: 1989
  ident: ref8
  publication-title: Learning Automata An Introduction
– ident: ref1
  doi: 10.1109/TPAMI.1986.4767779
– year: 1988
  ident: ref6
  publication-title: The Theory of Games
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Snippet A parallel stochastic algorithm for relaxation labeling is analyzed. For the case of symmetric compatibility functions, it is proved that time algorithm will...
A parallel stochastic algorithm for relaxation labelling is analyzed. For the case of symmetric compatibility functions, it is proved that the algorithm will...
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SubjectTerms Algorithm design and analysis
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer vision
Convergence
Exact sciences and technology
Image converters
Iterative algorithms
Labeling
Learning automata
Machine intelligence
Pattern recognition. Digital image processing. Computational geometry
Stochastic processes
Title Analysis of stochastic automata algorithm for relaxation labelling
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