| Layered estimation of atmospheric mesoscale dynamics from satellite imagery P Héas, E Mémin, N Papadakis, A Szantai IEEE Transactions on Geoscience and Remote Sensing 45 (12), 4087-4104, 2007 | 95 | 2007 |
| Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning P Héas, M Datcu IEEE Transactions on Geoscience and Remote Sensing 43 (7), 1635-1647, 2005 | 87 | 2005 |
| Wavelets and optical flow motion estimation P Dérian, P Héas, C Herzet, E Mémin Numerical Mathematics: Theory, Methods and Applications 6 (1), 116-137, 2013 | 68 | 2013 |
| Dynamic consistent correlation-variational approach for robust optical flow estimation D Heitz, P Héas, E Mémin, J Carlier Experiments in fluids 45 (4), 595-608, 2008 | 68 | 2008 |
| Divergence-free wavelets and high order regularization S Kadri-Harouna, P Dérian, P Héas, E Mémin International journal of computer vision 103 (1), 80-99, 2013 | 63 | 2013 |
| Pressure image assimilation for atmospheric motion estimation T Corpetti, P Héas, E Mémin, N Papadakis Tellus A: Dynamic Meteorology and Oceanography 61 (1), 160-178, 2009 | 58 | 2009 |
| Three-dimensional motion estimation of atmospheric layers from image sequences P Héas, E Mémin IEEE transactions on geoscience and remote sensing 46 (8), 2385-2396, 2008 | 52 | 2008 |
| Power laws and inverse motion modelling: application to turbulence measurements from satellite images P Héas, E Mémin, D Heitz, PD Mininni Tellus A: Dynamic Meteorology and Oceanography 64 (1), 10962, 2012 | 37 | 2012 |
| Bayesian selection of scaling laws for motion modeling in images P Héas, E Mémin, D Heitz, PD Mininni 2009 IEEE 12th International Conference on Computer Vision, 971-978, 2009 | 31 | 2009 |
| Wavelet-based fluid motion estimation P Dérian, P Héas, C Herzet, É Mémin Scale Space and Variational Methods in Computer Vision: Third International …, 2012 | 30 | 2012 |
| Bayesian estimation of turbulent motion P Héas, C Herzet, E Mémin, D Heitz, PD Mininni IEEE transactions on pattern analysis and machine intelligence 35 (6), 1343-1356, 2012 | 27 | 2012 |
| Bayesian inference of models and hyperparameters for robust optical-flow estimation P Héas, C Herzet, E Mémin IEEE Transactions on Image Processing 21 (4), 1437-1451, 2011 | 26 | 2011 |
| Low-rank dynamic mode decomposition: An exact and tractable solution P Héas, C Herzet Journal of Nonlinear Science 32 (1), 8, 2022 | 21* | 2022 |
| Self-similar prior and wavelet bases for hidden incompressible turbulent motion P Héas, F Lavancier, S Kadri-Harouna SIAM Journal on Imaging Sciences 7 (2), 1171-1209, 2014 | 20 | 2014 |
| Image assimilation for motion estimation of atmospheric layers with shallow-water model N Papadakis, P Héas, É Mémin Asian Conference on Computer Vision, 864-874, 2007 | 19 | 2007 |
| Optimal low-rank dynamic mode decomposition P Héas, C Herzet 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 15 | 2017 |
| Wavelets to reconstruct turbulence multifractals from experimental image sequences P Derian, P Héas, E Memin Seventh International Symposium on Turbulence and Shear Flow Phenomena, 2011 | 12 | 2011 |
| Scale space exploration for mining image information content M Ciucu, P Heas, M Datcu, JC Tilton Pacific-Asia Conference on Knowledge Discovery and Data Mining, 118-133, 2002 | 10 | 2002 |
| Generalized kernel-based dynamic mode decomposition P Héas, C Herzet, B Combes ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 9 | 2020 |
| Dense motion estimation from eye-safe aerosol lidar data P Dérian, P Héas, É Mémin, S Mayor 25th International Laser Radar Conference, 2010 | 9 | 2010 |
