Parallel transport in shape analysis: a scalable numerical scheme
Computer Vision and Pattern Recognition
2017-11-27 v1 Differential Geometry
Machine Learning
Abstract
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake. This complexity arises from the always-increasing dimension of the data, and the absence of closed-form expressions to basic operations such as the Riemannian logarithm. In this paper, we adapt a generic numerical scheme recently introduced for computing parallel transport along geodesics in a Riemannian manifold to finite-dimensional manifolds of diffeomorphisms. We provide a qualitative and quantitative analysis of its behavior on high-dimensional manifolds, and investigate an application with the prediction of brain structures progression.
Cite
@article{arxiv.1711.08725,
title = {Parallel transport in shape analysis: a scalable numerical scheme},
author = {Maxime Louis and Alexandre Bône and Benjamin Charlier and Stanley Durrleman},
journal= {arXiv preprint arXiv:1711.08725},
year = {2017}
}