Use of random graph parsing for scene labelling by probabilistic relaxation

Abstract A generalization of the relaxation labelling model, in which we operate on graphs of labels rather than pairs of labels attached to segmentation objects is proposed. The proposed approach is based on efficient, O(n2), parsing for graph grammars (Flasinski, 1993). To take into account all variations of an ambiguous (distorted) scene under study, a probabilistic description of the scene is needed. Random graphs are proposed here for such a description.

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