Bayesian object matching
Stan Li
Journal of Applied Statistics, 1998, vol. 25, issue 3, 425-443
Abstract:
A Bayesian approach to object matching is presented. An object and a scene are each represented by features, such as critical points, line segments and surface patches, constrained by unary properties and contextual relations. The matching is presented as a labeling problem, where each feature in the scene is assigned (associated with) a feature of the known model objects. The prior distribution of a scene's labeling is modeled as a Markov random field, which encodes the between-object constraints. The conditional distribution of the observed features labeled is assumed to be Gaussian, which encodes the within-object constraints. An optimal solution is defined as a maximum a posteriori estimate. Relationships with previous work are discussed. Experimental results are shown.
Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:25:y:1998:i:3:p:425-443
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DOI: 10.1080/02664769823142
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