E. H. Adelson and J. R. Bergen, Spatiotemporal energy models for the perception of motion, Journal of the Optical Society of America A, vol.2, issue.2, pp.284-99, 1985.
DOI : 10.1364/JOSAA.2.000284

D. Dong, Maximizing Causal Information of Natural Scenes in Motion, Dynamics of Visual Motion Processing, pp.261-282, 2010.
DOI : 10.1007/978-1-4419-0781-3_12

G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto, Dynamic textures, International Journal of Computer Vision, vol.51, issue.2, pp.91-109, 2003.
DOI : 10.1023/A:1021669406132

D. J. Field, Relations between the statistics of natural images and the response properties of cortical cells, Journal of the Optical Society of America A, vol.4, issue.12, pp.2379-2394, 1987.
DOI : 10.1364/JOSAA.4.002379

B. Galerne, Stochastic image models and texture synthesis, 2011.
URL : https://hal.archives-ouvertes.fr/tel-00595283

B. Galerne, Y. Gousseau, and J. M. Morel, Micro-Texture Synthesis by Phase Randomization, Image Processing On Line, vol.1, 2011.
DOI : 10.5201/ipol.2011.ggm_rpn

R. L. Gregory, Perceptions as Hypotheses, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.290, issue.1038, pp.181-197, 1038.
DOI : 10.1098/rstb.1980.0090

M. Jogan and A. A. Stocker, Signal Integration in Human Visual Speed Perception, Journal of Neuroscience, vol.35, issue.25, pp.9381-9390, 2015.
DOI : 10.1523/JNEUROSCI.4801-14.2015

O. Nestares, D. Fleet, and D. Heeger, Likelihood functions and confidence bounds for total-least-squares problems, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.523-530, 2000.
DOI : 10.1109/CVPR.2000.855864

P. Sanz-leon, I. Vanzetta, G. S. Masson, and L. U. Perrinet, Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception, Journal of Neurophysiology, vol.107, issue.11, pp.3217-3226, 2012.
DOI : 10.1152/jn.00737.2011

C. Simoncini, L. U. Perrinet, A. Montagnini, P. Mamassian, and G. S. Masson, More is not always better: adaptive gain control explains dissociation between perception and action, Nature Neuroscience, vol.23, issue.11, pp.151596-1603, 2012.
DOI : 10.1523/JNEUROSCI.4233-03.2004

G. Sotiropoulos, A. R. Seitz, and P. , Contrast dependency and prior expectations in human speed perception, Vision Research, vol.97, issue.0, pp.16-23, 2014.
DOI : 10.1016/j.visres.2014.01.012

A. A. Stocker and E. P. Simoncelli, Noise characteristics and prior expectations in human visual speed perception, Nature Neuroscience, vol.93, issue.4, pp.578-585, 2006.
DOI : 10.1038/nn1669

M. Unser and P. Tafti, An Introduction to Sparse Stochastic Processes, 2014.
DOI : 10.1017/CBO9781107415805

M. Unser, P. D. Tafti, A. Amini, and H. Kirshner, A Unified Formulation of Gaussian Versus Sparse Stochastic Processes—Part II: Discrete-Domain Theory, IEEE Transactions on Information Theory, vol.60, issue.5, pp.603036-3051, 2014.
DOI : 10.1109/TIT.2014.2311903

L. Y. Wei, S. Lefebvre, V. Kwatra, and G. Turk, State of the art in example-based texture synthesis, Eurographics 2009, State of the Art Report, EG-STAR. Eurographics Asso- ciation, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00606853

X. Wei and A. A. Stocker, Efficient coding provides a direct link between prior and likelihood in perceptual bayesian inference, pp.1313-1321, 2012.

Y. Weiss and D. J. Fleet, Velocity likelihoods in biological and machine vision, Probabilistic Models of the Brain: Perception and Neural Function, pp.81-100, 2001.

Y. Weiss, E. P. Simoncelli, and E. H. Adelson, Motion illusions as optimal percepts, Nature Neuroscience, vol.35, issue.1, pp.598-604, 2002.
DOI : 10.1038/nn0602-858

G. S. Xia, S. Ferradans, G. Peyré, and J. F. Aujol, Synthesizing and Mixing Stationary Gaussian Texture Models, SIAM Journal on Imaging Sciences, vol.7, issue.1, pp.476-508, 2014.
DOI : 10.1137/130918010
URL : https://hal.archives-ouvertes.fr/hal-00816342

R. A. Young and R. M. Lesperance, The Gaussian Derivative model for spatial-temporal vision: II. Cortical data, Spatial Vision, vol.14, issue.3, pp.321-390, 2001.
DOI : 10.1163/156856801753253591