Issue No. 04 - April (1981 vol. 3)
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.
Marc Berthod , INRIA, Rocquencourt, France.
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.
M. Berthod and O. D. Faugeras, "Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 3, no. , pp. 412-424, 1981.