Matching deformed Delaunay triangulations

This paper describes a Bayesian framework for matching graphs whose structure can be described in terms of triangular faces. Relational structures of this sort are ubiquitous in intermediate level computer vision, being exemplified by Delaunay graphs which represent the Voronoi tessellation of an image plane. Our matching process is realised in terms of probabilistic relaxation. The novelty of our method stems from its use of a support function specified in terms of face-units of the graphs under match. In this way we draw on more expressive constraints than is possible at the level of edge-units alone. In order to apply this new relaxation process to the matching of realistic imagery requires model of the compatibility between faces of the data and model graphs. We present a particularly simple compatibility model that is entirely devoid of free parameters. It requires only knowledge of the numbers of nodes, edges and faces in the model graph. The resulting matching scheme is evaluated on radar images and compared with its edge-based counterpart