Registro:

Referencias:

• Adrian, R., Particle imaging techniquesfor experimental fluid mechanics (1991) Annal. Rev. Fluid Mech., 23, pp. 261-304
• Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R., A database and evaluation methodology for optical flow (2007) Computer Vision, pp. 1-8. , 2007, ICCV 2007. IEEE 11th International Conference on, vol., no., 14-21 Oct. 2007
• Benzi, R., Scardovelli, R., Intermittency of two-dimensional decaying turbulence (1995) Europhys. Lett., 29, pp. 371-376
• Bergen, J., Burt, P., Hingorani, R., Peleg, S., A 3-frame algorithm for estimating two-component image motion (1992) IEEE Trans. Pattern Anal. Mach. Intell., 14 (9), pp. 886-895
• Berliner, M., Wikle, C., Milliff, R., Multiresolution wavelet analyses in hierarchical Bayesian turbulence models. Bayesian inference in wavelet based models (1999) Lect. Notes Statis., 141, pp. 341-349
• Bernard, D., Boffetta, G., Celani, A., Falkovich, G., Conformal invariance in two-dimensional turbulence (2006) Nature Physics., 2, pp. 124-128
• Boffetta, G., Energy and enstrophy fluxes in the double cascade of two-dimensional turbulence (2007) J. Fluid Mech., 589, pp. 253-260
• Carlier, J., Wieneke, B., (2005) Report 1 on production and diffusion of fluid mechanics images and data, , http://www.fluid.irisa.fr, Online at: Fluid project deliverable 1.2
• Charney, J., Geostrophic turbulence (1971) J. Atmos. Sci., 28, pp. 1087-1095
• Cho, J., Lindborg, E., Horizontal velocity structure functions in the upper troposphere and lower stratosphere 1. observations (2001) J. Geophys. Res., 106, pp. 223-232
• Corpetti, T., Heas, P., Memin, E., Papadakis, N., Pressure image assimilation for atmospheric motion estimation (2009) Tellus A: Dynamic Meteorology and Oceanography., 61, pp. 160-178
• Corpetti, T., Mémin, E., Pérez, P., Dense estimation of fluid flows (2002) Pattern Anal. Mach. Intel., 24 (3), pp. 365-380
• Dewan, E., Saturated-cascade similitude theory of gravity wave spectra (1997) J. Geophys. Res., 102, pp. 799-818
• Falkovich, G., Gawȩdzki, K., Vergazzola, M., Particles and fieldsin fluid turbulence (2001) Rev. Mod. Phys., 73, pp. 913-975
• Fitzpatrick, J., The existence of geometrical density-image transformations corresponding to object motion (1988) Comput. Vision, Graphics, Image Proc., 44 (2), pp. 155-174
• Frisch, U., (1995) Turbulence: The Legacy of A.N. Kolmogorov, , Cambridge, Cambridge University Press
• Gotoh, T., Fukayama, D., Nakano, T., Velocity field statistics in homogeneous steady turbulence using a highresolution direct numerical simulation (2002) Physics of Fluids, 14, pp. 1065-1081
• Gull, S.F., Developmentsin maximum-entropy data analysis (1989) In Maximum Entropy and Bayesian Methods, pp. 53-71. , J. Skilling (ed.), Kluwer Academic, Dordrecht
• Heas, P., Memin, E., Three-dimensional motion estimation of atmospheric layers from image sequences (2008) IEEE trans. on Geo. and Rem. Sensing., 46 (8), pp. 2385-2396
• Heas, P., Memin, E., Heitz, D., Mininni, P., Bayesian selection of scaling laws for motion modeling in images (2009) In International Conference on Computer Vision (ICCV'09), , Kyoto, Japan
• Heas, P., Memin, E., Papadakis, N., Szantai, A., Layered estimation of atmospheric mesoscale dynamics from satellite imagery (2007) IEEE trans. on Geo. and Rem. Sensing., 45 (12), pp. 4087-4104
• Heas, P., Memin, E., Papadakis, N., Szantai, A., Layered estimation of atmospheric mesoscale dynamics from satellite imagery (2007) IEEE trans. on Geo. and Rem. Sensing., 45 (12), pp. 4087-4104
• Heitz, D., Carlier, J., Arroyo, G., (2007), Final Report on the Evaluation of the Tasks of the Workpackage 2. Technical report, Deliverable 5.4, Cemagref, Rennes, France; Heitz, D., Memin, E., Schnoerr, C., Variational fluid flow measurements from image sequences: synopsis and perspectives (2010) Exp. Fluids., 48 (3), pp. 369-393
• Hoar, T.J., Milliff, R.F., Nychka, D., Wikle, C.K., Berliner, L.M., Windsfrom a Bayesian hierarchical model: computation for atmosphere-ocean research (2003) J. Comp. and Graph. Statis., 12 (4), pp. 781-807
• Horn, B., Schunck, B., Determining optical flow (1981) Artificial Intelligence., 17, pp. 185-203
• Jaynes, E.T., (2003) Probability Theory: The Logic of Science, , Cambridge, Cambridge University Press
• Kolmogorov, A., The local structure of turbulence in inompressible viscous fluid for very large Reynolds number (1941) Dolk. Akad. Nauk SSSR., 30, pp. 301-305
• Kraichnan, R., Inertial rangesin two-dimensional turbulence (1967) Phys. Fluids., 10, pp. 1417-1423
• Kurien, S., L'vov, V.S., Procaccia, I., Sreenivasan, K.R., The scaling structure of the velocity statistics in atmospheric boundary layer (2000) Phys. Rev. E(Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics)., 61 (1), pp. 407-421
• Leese, J., Novack, C., Clark, B., An automated technique for obtained cloud motion from geosynchronous satellite data using cross correlation (1971) J. Appl. Met., 10, pp. 118-132
• Lindborg, E., Can the atmospheric kinetic energy spectrum be explained by two-dimensional turbulence (1999) J. Fluid Mech., 388, pp. 259-288
• Lindborg, E., Horizontal wavenumber spectra of vertical vorticity and horizontal divergence in the upper troposphere and lower stratosphere (2007) J. Atmos. Sci., 64 (3), pp. 1017-1025
• Lindborg, E., Cho, J., Horizontal velocity structure functions in the upper troposphere and lower stratosphere 2 theoretical considerations (2001) J. Geophys. Res., 106, pp. 233-241
• Liu, T., Shen, L., Fluid flow and optical flow (2008) J. Fluid Mech., 614, pp. 253-291
• Lucas, B.D., Kanade, T., An iterative image registration technique with an application to stereo vision (1981) In Proceedings of the 7th international joint conference on Artificial intelligence, 2, pp. 674-679. , (IJCAI'81), Vol. 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
• MacKay, D.J.C., Bayesian interpolation (1992) Neural Computation., 4 (3), pp. 415-447
• MacKay, D.J.C., (2003) Information Theory, Inference, and Learning Algorithms, , Cambridge, Cambridge University Press
• Monin, A.S., Yaglom, A.M., (1975) Statistical Fluid Mechanics: Mechanics of Turbulence, , New York, MIT Press
• Nastrom, G., Jasperson, W., Gage, K., Kinetic energy spectrum of large and mesoscale atmospheric processes (1984) Nature., 310, pp. 36-38
• Nastrom, G.D., Eaton, F.D., Turbulence eddy dissipation rates from radar observations at 5-20 km at White Sands Missile Range, New Mexico (1997) J. Geophys. res., 102, pp. 19495-19506
• Nocedal, J., Wright, S.J., (1999) Numerical Optimization., , Springer Seriesin OperationsRes earch, Springer-Verlag, New York, NY
• Papadakis, N., Memin, E., Variational assimilation of fluid motion from image sequences (2008) SIAM J. Imaging Sci., 1 (4), pp. 343-363
• Pedlosky, P., (1987) Geophysical Fluid Dynamics, , Springer, New York
• Robert, C.P., (2007) The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, p. 577. , Springer
• Skilling, J., The eigenvalues of mega-dimensional matrices (1989) Maximum Entropy and Bayesian Methods, pp. 455-466. , Cambridge, England
• Taylor, M.A., Kurien, S., Eyink, G.L., Recovering isotropic statistics in turbulence simulations: The kolmogorov 4/ 5th-law (2003) Phys. rev. E., 2, p. 026310
• Tung, K.K., The k 3 and k 5/3 energy spectrum of the atmospheric turbulence: quasi-geostrophic two level model simulation (2003) J. Atmos. Sci., 60, pp. 824-835
• Weickert, J., Schnörr, C., A theoretical framework for convex regularizersin pde-based computation of image motion (2004) Int. J. Comp. Vis., 45 (3), pp. 245-264
• Wikle, C.K., Milliff, R.F., Nychka, D., Berliner, L.M., Spatio-temporal hierarchical Bayesian modeling: tropical ocean surface winds (2001) J. Amer. Statis. Assoc., 96, pp. 382-397
• Yuan, J., Schnoerr, C., Memin, E., Discrete orthogonal decomposition and variational fluid flow estimation (2007) J. Math. Imaging & Vision., 28, pp. 67-80

Citas:

---------- APA ----------

---------- CHICAGO ----------

---------- MLA ----------

---------- VANCOUVER ----------