Statistical physics, mixtures of distributions, and the EM algorithm
Neural Computation
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterizing spatio-temporal patterns for disease discrimination in cardiac echo videos
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Deformable density matching for 3D non-rigid registration of shapes
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Information-theoretic matching of two point sets
IEEE Transactions on Image Processing
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In this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. To quantify the divergence between the probability distributions --- specifically Mixture of Gaussians --- estimated from the given point sets, we use a recently developed information-theoretic measure called Jensen-Renyi (JR) divergence. We evaluate a closed-form JR divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components. We derive the analytic gradient of the divergence measure with respect to the non-rigid registration parameters, and apply it to numerical optimization of the group-wise registration, leading to a computationally efficient and accurate algorithm. We validate our approach on synthetic data, and evaluate it on 3D cardiac shapes.