Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
Geodesic Shooting for Computational Anatomy
Journal of Mathematical Imaging and Vision
Transport of Relational Structures in Groups of Diffeomorphisms
Journal of Mathematical Imaging and Vision
Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Simple geodesic regression for image time-series
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Temporally-dependent image similarity measure for longitudinal analysis
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Polynomial regression on riemannian manifolds
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Analysis of longitudinal shape variability via subject specific growth modeling
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
A hierarchical geodesic model for diffeomorphic longitudinal shape analysis
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Geodesic shape regression in the framework of currents
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Geodesic Regression and the Theory of Least Squares on Riemannian Manifolds
International Journal of Computer Vision
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Registration of image-time series has so far been accomplished (i) by concatenating registrations between image pairs, (ii) by solving a joint estimation problem resulting in piecewise geodesic paths between image pairs, (iii) by kernel based local averaging or (iv) by augmenting the joint estimation with additional temporal irregularity penalties. Here, we propose a generative model extending least squares linear regression to the space of images by using a second-order dynamic formulation for image registration. Unlike previous approaches, the formulation allows for a compact representation of an approximation to the full spatio-temporal trajectory through its initial values. The method also opens up possibilities to design image-based approximation algorithms. The resulting optimization problem is solved using an adjoint method.