Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Unsupervised Learning of an Atlas from Unlabeled Point-Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Shape Model without Using Landmarks
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Least biased target selection in probabilistic atlas construction
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
3D statistical shape modeling of long bones
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
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We present an extension of the symmetric ICP algorithm that is unbiased for an arbitrary number (N≥2) of shapes, using rigid transformations and scaling. The method does not require the selection of a reference shape or registration order and hence it is unbiased towards any of the registered shapes. The functional to be minimized is non-linear in the transformation parameters and thus computationally complex. We therefore propose a first order approximation that estimates the transformation parameters in a closed form, with computational complexity $\mathcal{O}(N^{2})$. Using a set of wrist bones, we show that the least-squares minimization and the proposed approximation converge to the same solution. Experiments also show that the proposed algorithms lead to smaller registration errors than algorithms that select a reference shape or register to an evolving mean shape. The low computational cost and trivial parallelization enable the alignment of large numbers of bones.