Robust NonRigid Point Matching

Point Matching: Problem & Algorithm 
I have been primarily interested in the problem of solving for high dimensional nonrigid deformations given two sets of feature points, for which the correspondence information is unknown beforehand. This is what we call the "nonrigid point matching problem."

We have designed a new nonrigid point matching algorithm
that is capable of estimating both complex nonrigid
transformations as well as meaningful correspondences
between two sets of points. The effectiveness of the algorithm
comes from two techniques: softassign and deterministic
annealing.
The algorithm is very robust. First, it tolerates noises. Second, it can automatically evalute all evidence and reject outliers. Finally, it demonstrates stong ability in overcoming local minima and bad initializations. The algorithm is hence called the "robust point matching algorithm (RPM)."

A demo for robust point matching. 
This package of MATLAB Mfiles provides a demo for the
Robust Point Matching (RPM) algorithm. Five example data
pointsets are included. We also provide a simple GUI to
load the data and start the demo. All the source code
(Mfiles) required to execute RPM are included. The code is provided under the terms of the GNU General Public License with an explicit clause permitting the Mfiles to be executed from within the Matlab environment.

Some references for robust point matching: 
(1) "A new algorithm for nonrigid point matching", H. Chui and A. Rangarajan, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2000, volume 2, pages 4451. Download the postscript file. Honorable mention of Best Student Paper Award at CVPR'2000.
(2) "A feature registration framework using mixture models",
(3) "A unified framework for brain anatomical feature registration",
(4) "Registration of cortical anatomical structures via 3D robust
point matching",
(5) "A robust point matching algorithm for autoradiograph alignment",
Further references on robust point matching can be found at Anand Rangarajan's homepage. 
Acknowledgement: This work is partially supported by the National Science Foundation Grant IIS9906081. 