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Issue No.04 - April (1981 vol.3)
pp: 412-424
Olivier D. Faugeras , MEMBER, IEEE, Image Processing Institute, University of Southern California, Los Angeles, CA 90007; INRIA, Rocquencourt, France; University of Paris XI, Paris, France.
ABSTRACT
We approach the problem of labeling a set of objects from a quantitative standpoint. We define a world model in terms of transition probabilities and propose a definition of a class of global criteria that combine both ambiguity and consistency. A projected gradient algorithm is developed to minimize the criterion. We show that the minimization procedure can be implemented in a highly parallel manner. Results are shown on several examples and comparisons are made with relaxation labeling techniques.
CITATION
Olivier D. Faugeras, "Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.3, no. 4, pp. 412-424, April 1981, doi:10.1109/TPAMI.1981.4767127
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