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NIPS
1997

Graph Matching with Hierarchical Discrete Relaxation

10 years 5 months ago
Graph Matching with Hierarchical Discrete Relaxation
Our aim in this paper is to develop a Bayesian framework for matching hierarchical relational models. Such models are widespread in computer vision. The framework that we adopt for this study is provided by iterative discrete relaxation. Here the aim is to assign the discrete matches so as to optimise a global cost function that draws information concerning the consistency of match from di€erent levels of the hierarchy. Our Bayesian development naturally distinguishes between intra-level and inter-level constraints. This allows the impact of reassigning a match to be assessed not only at its own (or peer) level of representation, but also upon its parents and children in the hierarchy. We illustrate the e€ectiveness of the technique in the matching of line-segment groupings in synthetic aperture radar (SAR) images of rural scenes. Ó 1999 Elsevier Science B.V. All rights reserved.
Richard C. Wilson, Edwin R. Hancock
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where NIPS
Authors Richard C. Wilson, Edwin R. Hancock
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