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
A unified information-theoretic approach to groupwise non-rigid registration and model building
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Information-theoretic matching of two point sets
IEEE Transactions on Image Processing
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This paper proposes a novel and robust approach to the groupwise point-sets registration problem in the presence of large amounts of noise and outliers. Each of the point sets is represented by a mixture of Gaussians and the point-sets registration is treated as a problem of aligning the multiple mixtures. We develop a novel divergence measure which is defined between any arbitrary number of probability distributions based on L2 distance, and we call this new divergence measure "Generalized L2-divergence ". We derive a closed-form expression for the Generalized-L2 divergence between multiple Gaussian mixtures, which in turn leads to a computationally efficient registration algorithm. This new algorithm has an intuitive interpretation, is simple to implement and exhibits inherent statistical robustness. Experimental results indicate that our algorithm achieves very good performance in terms of both robustness and accuracy.