Learning Distributions of Shape Trajectories from Longitudinal Datasets: A Hierarchical Model on a Manifold of Diffeomorphisms

@article{Bne2018LearningDO,
  title={Learning Distributions of Shape Trajectories from Longitudinal Datasets: A Hierarchical Model on a Manifold of Diffeomorphisms},
  author={Alexandre B{\^o}ne and Olivier Colliot and Stanley Durrleman},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={9271-9280}
}
We propose a method to learn a distribution of shape trajectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points. The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this trajectory both in terms of geometry and time dynamics. First, we formulate a non-linear mixed-effects statistical model as the combination of a generic statistical model for… 
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