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This 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 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.
Síntese de Imagens
Reconhecimento de Padrões
Visualização de Dados
Visão por Computador
Aplicações em Medicina
Modelagem e Visualização
Animação e Multimídia
Processamento de Imagens
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