title: Improving consistency and reducing ambiguity in stochastic labeling: an optimization approach.
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.
authors.affiliation: MEMBER, IEEE, Image Processing Institute, University of Southern California, Los Angeles, CA 90007; INRIA, Rocquencourt, France; University of Paris XI, Paris, France.
authors.forename: O D
authors.initials: OD
authors.lastname: Faugeras
authors.forename: M
authors.initials: M
authors.lastname: Berthod