A Bayesian compatibility model for graph matching
Pattern Recognition Letters
An energy function and continuous edit process for graph matching
Neural Computation
An application of "agent-oriented" techniques to symbolic matching and object recognition
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
The myth of the double-blind review?: author identification using only citations
ACM SIGKDD Explorations Newsletter
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The paper describes a novel approach to relational matching problems in machine vision. Rather than matching static scene descriptions, the approach adopts an active representation of the data to be matched. This representation is iteratively reconfigured to increase its degree of topological congruency with the model relational structure in a reconstructive matching process. The active reconfiguration of relational structures is controlled by a MAP update process. The final restored graph representation is optimal in the sense that it has maximum a posteriori probability with respect to the available attributes for the objects under match. The benefits of the technique are demonstrated experimentally on the matching of cluttered synthetic aperture radar data to a model in the form of a digital map. The operational limits of the method are established in a simulation study.