(Enter summary)
Abstract: This thesis develops an optimisation framework within which the principle of least commitment
can be applied to the solution of ambiguous consistent labelling problems. The
natural optimisation method for such an approach is a hybrid genetic algorithm. The starting
point is the refinement of an existing Bayesian approach to inexact labelling problems.
Two criteria are presented which measure consistency in terms of the edit distance between
labelled configurations and a dictionary of legal... (Update)
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BibTeX entry: (Update)
R. Myers and Edwin R. Hancock. Genetic algorithms for ambiguous labelling problems. In Proceedings of the International Workshop Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 345-360, Venice (Italy), 21.-23. May 1997. Springer-Verlag, Berlin (Germany). yEEA87342/97 ga97aMyers. http://citeseer.ist.psu.edu/myers99genetic.html More
@inproceedings{ myers97genetic,
author = "Richard Myers and Edwin R. Hancock",
title = "Genetic Algorithms for Ambiguous Labelling Problems",
booktitle = "Energy Minimization Methods in Computer Vision and Pattern Recognition",
pages = "345-360",
year = "1997",
url = "citeseer.ist.psu.edu/myers99genetic.html" }
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[Article contains additional citations not shown here]
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