J. Ashburner, A fast diffeomorphic image registration algorithm, NeuroImage, vol.38, issue.1, pp.95-113, 2007.
DOI : 10.1016/j.neuroimage.2007.07.007

D. M. Blei and P. I. Frazier, Distance dependent chinese restaurant processes, The Journal of Machine Learning Research, vol.12, pp.2461-2488, 2011.

C. Buerger, T. Schaeffter, and A. P. King, Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation, Medical Image Analysis, vol.15, issue.4, pp.551-564, 2011.
DOI : 10.1016/j.media.2011.02.009

S. Durrleman, S. Allassonnì-ere, and S. Joshi, Sparse Adaptive Parameterization of Variability in Image Ensembles, International Journal of Computer Vision, vol.67, issue.2, pp.161-183, 2013.
DOI : 10.1007/s11263-012-0556-1
URL : https://hal.archives-ouvertes.fr/hal-00817565

S. Ghosh, E. Sudderth, M. Loper, and M. Black, From deformations to parts: Motionbased segmentation of 3D objects, pp.2006-2014, 2012.

S. Ghosh, A. B. Ungureanu, E. B. Sudderth, and D. M. Blei, Spatial distance dependent chinese restaurant processes for image segmentation, p.NIPS, 2011.

M. Girolami and B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011.
DOI : 10.1111/j.1467-9868.2010.00765.x

M. Hernandez, M. N. Bossa, and S. Olmos, Registration of anatomical images using geodesic paths of diffeomorphisms parameterized with stationary vector fields, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409126

S. Kim, P. Smyth, and H. Stern, A Nonparametric Bayesian Approach to Detecting Spatial Activation Patterns in fMRI Data, MICCAI 2006, Part II, pp.217-224, 2006.
DOI : 10.1007/11866763_27

M. Lorenzi, N. Ayache, G. B. Frisoni, and X. Pennec, LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm, NeuroImage, vol.81, 2013.
DOI : 10.1016/j.neuroimage.2013.04.114
URL : https://hal.archives-ouvertes.fr/hal-00819895

S. Marsland and R. Mclachlan, A Hamiltonian Particle Method for Diffeomorphic Image Registration, IPMI, vol.20, pp.396-407, 2007.
DOI : 10.1007/978-3-540-73273-0_33

S. Marsland, R. Mclachlan, K. Modin, and M. Perlmutter, Geodesic Warps by Conformal Mappings, International Journal of Computer Vision, vol.10, issue.2, pp.1-11, 2012.
DOI : 10.1007/s11263-012-0584-x
URL : http://arxiv.org/abs/1203.3982

R. M. Neal, MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo 54, pp.113-162, 2010.

C. Seiler, X. Pennec, and M. Reyes, Capturing the multiscale anatomical shape variability with polyaffine transformation trees, Medical Image Analysis, vol.16, issue.7, 2012.
DOI : 10.1016/j.media.2012.05.011
URL : https://hal.archives-ouvertes.fr/hal-00813866

S. Sommer, F. Lauze, M. Nielsen, and X. Pennec, Sparse Multi-Scale Diffeomorphic Registration: The Kernel Bundle Framework, Journal of Mathematical Imaging and Vision, vol.45, issue.1, Suppl.??1, pp.1-17, 2012.
DOI : 10.1007/s10851-012-0409-0
URL : https://hal.archives-ouvertes.fr/hal-00813868

D. W. Thompson, On Growth and Form, 1992.

T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, Diffeomorphic demons: Efficient non-parametric image registration, NeuroImage, vol.45, issue.1, pp.61-72, 2009.
DOI : 10.1016/j.neuroimage.2008.10.040
URL : https://hal.archives-ouvertes.fr/inserm-00349600

X. Wang, E. E. Grimson, and C. F. Westin, Tractography segmentation using a hierarchical Dirichlet processes mixture model, NeuroImage, vol.54, issue.1, pp.290-302, 2011.
DOI : 10.1016/j.neuroimage.2010.07.050