IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Generalized L2-Divergence and Its Application to Shape Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence
International Journal of Computer Vision
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Attribute Vector Guided Groupwise Registration
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Affine iterative closest point algorithm for point set registration
Pattern Recognition Letters
A Novel Kernel Correlation Model with the Correspondence Estimation
Journal of Mathematical Imaging and Vision
Non-rigid shape registration: a single linear least squares framework
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Modeling the effect of motion at encoding and retrieval for same and other race face recognition
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
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Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.