Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach

Authors:
Olivier D. Faugeras;Marc Berthod
Affiliations:
MEMBER, IEEE, Image Processing Institute, University of Southern California, Los Angeles, CA 90007/ INRIA, Rocquencourt, France/ University of Paris XI, Paris, France.;INRIA, Rocquencourt, France.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1981

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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.