Graph-Based Methods for Vision: A Yorkist Manifesto

This paper provides an overview of our joint work on graph-matching. We commence by reviewing the literature which has motivated this work. We then proceed to review our contributions under the headings of 1) the probabilistic framework, 2) search and optimisation, 3) matrix methods, 4) segmentation and grouping, 5) learning and 6) applications.

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[67]  Edwin R. Hancock,et al.  New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

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[72]  Edwin R. Hancock,et al.  A Maximum Likelihood Framework for Grouping and Segmentation , 2001, EMMCVPR.

[73]  Edwin R. Hancock,et al.  Inexact Graph Matching with Genetic Search , 1996, SSPR.

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[76]  Horst Bunke,et al.  A graph distance metric based on the maximal common subgraph , 1998, Pattern Recognit. Lett..

[77]  Edwin R. Hancock,et al.  Structural Matching by Discrete Relaxation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..