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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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 16; H. 5; S. 538 - 543
Hauptverfasser:	Sastry, P.S., Thathachar, M.A.L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.05.1994 IEEE Computer Society
Schlagworte:	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 Learning Relaxation Computer vision Parallel algorithm Learning automata Constraint satisfaction Stochastic algorithm
ISSN:	0162-8828
Online-Zugang:	Volltext
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