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A Feature Registration Framework using Mixture Models
A Feature Registration Framework using Mixture Models
Haili Chui and Anand Rangarajan
Departments of Electrical Engineering and Diagnostic Radiology
Yale University, New Haven, CT 06520, USA
Abstract
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 de-
terministic 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 perfor-
mance on 2D and 3D data.
1 Introduction
Feature-based registration problems frequently arise in
the domains of computer vision and medical imaging. With
the salient structures in two images represented as compact
geometrical entities (e.g. points, curves, surfaces), we need
to find the spatial transformation/mapping as well as the
correspondence between them. Point features, represented
basically by the point locations, are the simplest form of
features. However, the resulting point matching problem
can be quite difficult because of various factors.
One common factor is noise arising from the processes
of image acquisition and feature extraction. The presence of
noise makes it difficult to decide on the extent to which the
features should be exactly matched. Another factor is the
existence of outliers—many point features may exist in one
point-set that have no corresponding points (homologies) in
the other and hence need to be rejected during the matching
process. Finally, the geometric transformations may need
to incorporate high dimensional non-rigid mappings in or-
der to account
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