A feature registration framework using mixture models

We formulate feature registration problems as maximum likelihood or Bayesian maximum a posteriori estimation problems using mixture models. An EM-like algorithm is proposed to jointly solve for the feature correspondences as well as the geometric transformations. A novel aspect of our approach is the embedding of the EM algorithm within a deterministic annealing scheme in order to directly control the fuzziness of the correspondences. The resulting algorithm-termed mixture point matching (MPM)-can solve for both rigid and high dimensional (thin-plate spline-based) non-rigid transformations between point sets in the presence of noise and outliers. We demonstrate the algorithm's performance on 2D and 3D data.