Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361788 | Pattern Recognition Letters | 2005 | 8 Pages |
Abstract
Comparing scene, pattern or object models to structures in images or determining the correspondence between two point sets are examples of attributed graph matching. In this paper we show how such problems can be posed as one of inference over hidden Markov random fields. We review some well known inference methods studied over past decades and show how the Junction Tree framework from Graphical Models leads to algorithms that outperform traditional relaxation-based ones.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Terry Caelli, Tiberio Caetano,