Caetano, TiberioCaelli, Terry2015-12-08August 20-0769525210http://hdl.handle.net/1885/33122We present a unified framework for modeling and solving invariant point pattern matching problems. Invariant features are encoded as potentials in a probabilistic graphical model. By using a specific kind of graph topology, different types of invariant matching models can be implemented via tree-width selection. Models with tree-widths 1, 2, 3 and 4 implement translation, similarity, affine and protective invariant point matching, respectively. The optimal match is then found by exploiting the Markov structure of the graph through the generalized distributive law in a dynamic programming setting. In the absence of noise in the point coordinates, the solutions found are optimal. Our early experiments suggest the approach is robust to outliers and moderate noise.Keywords: Graph theory; Markov processes; Mathematical models; Probability; Problem solving; Invariant point pattern matching; Markov structures; Probabilistic graphical models; Protective invariant point matching; Pattern matching200610.1109/ICPR.2006.1922015-12-08