Y. References-[-aat07-]-stéphanie-allassonnière, A. Amit, and . Trouvé, Towards a coherent statistical framework for dense deformable template estimation, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.69, issue.1, pp.3-29, 2007.

L. Stéphanie-allassonnière, X. Devilliers, and . Pennec, Estimating the template in the total space with the fréchet mean on quotient spaces may have a bias: a case study on vector spaces quotiented by the group of translations, Mathematical Foundations of Computational Anatomy (MFCA'15), 2015.

E. Stéphanie-allassonnière, A. Kuhn, and . Trouvé, Construction of Bayesian deformable models via a stochastic approximation algorithm: A convergence study, Bernoulli, vol.16, issue.3, pp.641-678, 2010.
DOI : 10.3150/09-BEJ229

A. Bhattacharya and R. Bhattacharya, Statistics on Riemannian manifolds: asymptotic distribution and curvature, Proceedings of the American Mathematical Society, pp.2959-2967, 2008.
DOI : 10.1090/S0002-9939-08-09445-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.192.3229

[. Bigot and B. Charlier, On the consistency of Fr??chet means in deformable models for curve and image analysis, Electronic Journal of Statistics, vol.5, issue.0, pp.1054-1089, 2011.
DOI : 10.1214/11-EJS633

D. Bontemps and S. Gadat, Bayesian methods for the Shape Invariant Model, Electronic Journal of Statistics, vol.8, issue.1, pp.1522-1568, 2014.
DOI : 10.1214/14-EJS933

URL : http://projecteuclid.org/download/pdfview_1/euclid.ejs/1410181224

J. Cleveland, W. Wu, and A. Srivastava, Norm-preserving constraint in the Fisher???Rao registration and its application in signal estimation, Journal of Nonparametric Statistics, vol.28, issue.2, pp.338-359, 2016.
DOI : 10.1007/s10827-012-0427-3

M. Stanley-durrleman, N. Prastawa, J. R. Charon, S. Korenberg, G. Joshi et al., Morphometry of anatomical shape complexes with dense deformations and sparse parameters, NeuroImage, vol.101, pp.35-49, 2014.
DOI : 10.1016/j.neuroimage.2014.06.043

M. Fréchet, Les elements aléatoires de nature quelconque dans un espace distancié, Annales de l'institut Henri Poincaré, pp.215-310, 1948.

U. Grenander and M. I. Miller, Computational anatomy: an emerging discipline, Quarterly of Applied Mathematics, vol.56, issue.4, pp.617-694, 1998.
DOI : 10.1090/qam/1668732

M. Sebastian-hitziger, A. Clerc, S. Gramfort, C. Saillet, T. Bénar et al., Jitter-adaptive dictionary learning-application to multi-trial neuroelectric signals. arXiv preprint, 2013.

[. Joshi, B. Davis, M. Jomier, and G. Gerig, Unbiased diffeomorphic atlas construction for computational anatomy, NeuroImage, vol.23, pp.151-160, 2004.
DOI : 10.1016/j.neuroimage.2004.07.068

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.3808

[. Karcher, Riemannian center of mass and mollifier smoothing, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.509-541, 1977.
DOI : 10.1002/cpa.3160300502

G. David and . Kendall, A survey of the statistical theory of shape, Statistical Science, pp.87-99, 1989.

S. Wilfrid and . Kendall, Probability, convexity, and harmonic maps with small image i: uniqueness and fine existence, Proceedings of the London Mathematical Society, vol.3, issue.2, pp.371-406, 1990.

A. Sebastian, A. Kurtek, W. Srivastava, and . Wu, Signal estimation under random time-warpings and nonlinear signal alignment, Advances in Neural Information Processing Systems 24, pp.675-683, 2011.

S. Laj-+-12-]-sidonie-lefebvre, J. Allassonnière, T. Jakubowicz, E. Lasne, and . Moulines, Aircraft classification with a low resolution infrared sensor, Machine Vision and Applications, pp.175-186, 2012.

[. Miolane, S. Holmes, and X. Pennec, Template shape estimation: correcting an asymptotic bias, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01350508

N. Miolane and X. Pennec, Biased Estimators on Quotient Spaces, Geometric Science of Information. Second International Conference, GSI 2015 Proceedings, 2015.
DOI : 10.1007/978-3-319-25040-3_15

URL : https://hal.archives-ouvertes.fr/hal-01203805

[. Pennec, Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements, Journal of Mathematical Imaging and Vision, vol.20, issue.10, pp.127-154, 2006.
DOI : 10.1007/s10851-006-6228-4

URL : https://hal.archives-ouvertes.fr/inria-00614994

M. Sabuncu, S. K. Balci, and P. Golland, Discovering Modes of an Image Population through Mixture Modeling, Proceeding of the MICCAI conference, pp.381-389, 2008.
DOI : 10.1007/978-3-540-85990-1_46

[. Ziezold, On Expected Figures and a Strong Law of Large Numbers for Random Elements in Quasi-Metric Spaces, Transactions of the Seventh Prague Conference on Information Theory, Statistical Decision Functions, Random Processes and of the 1974 European Meeting of Statisticians, pp.591-602, 1977.
DOI : 10.1007/978-94-010-9910-3_63

[. Zhang, N. Singh, and P. Fletcher, Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration, Information Processing in Medical Imaging, pp.37-48, 2013.
DOI : 10.1007/978-3-642-38868-2_4