Preprints
- F. Cérou, P. Héas, M. Rousset, Adaptive Reduced Multilevel Splitting, arXiv eprints.
- F. Cérou, P. Héas, M. Rousset, Entropy minimizing distributions are worst-case optimal importance proposals, arXiv eprints.
- P. Héas, C. Herzet, B. Combès Non-Linear Reduced Modeling by Generalized Kernel-Based Dynamic Mode Decomposition , arXiv eprints.
International Journals, Book Chapters
- P. Héas, F.Cérou, M. Rousset, Chilled Sampling for Uncertainty Quantification: A Motivation From A
Meteorological Inverse Problem, Inverse Problems, 2023
- P. Héas, O. Hautecoeur, R. Borde, 3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective, Physica Scripta, 2023.
- P. Héas, C. Herzet, Low-rank Dynamic Mode Decomposition: An Exact and Tractable Solution, Journal of Nonlinear Science, 2022
- P. Héas, Selecting Reduced Models in the Cross-Entropy Method, SIAM / ASA Journal on Uncertainty Quantification (JUQ), Volume 8, Issue 2, Page 511-538, 2020.
- C. Herzet, A. Drémeau, P. Héas, Model Reduction from Partial Observations, International Journal for Numerical Methods in Engineering, Wiley, Volume 113, Issue 3, pp.479-511, 2018
- P. Héas, C. Herzet, Reduced Modeling of Unknown Trajectories, Archives of Computational Methods in Engineering, Volume 25, Issue 1, pp 87-101, 2018
- P. Héas, A. Drémeau; C. Herzet. An Efficient Algorithm for Video Super-Resolution Based on a Sequential Model. SIAM Journal on Imaging Sciences, Volume 9, Issue 2, pp. 537–572, 2016.
- P. Héas, F. Lavancier, S. Kadri Harouna. Self-similar prior and wavelet bases for hidden incompressible turbulent motion. SIAM Journal on Imaging Sciences, Volume 7, Issue 2, pp. 1171-1209, 2014.
- S. Kadri Harouna, P Dérian, P. Héas, E. Mémin. Divergence-free wavelets and high-order regularization. International Journal of Computer Vision, Volume 103, Issue 1, pp 80-99, May 2013.
- P Dérian, P. Héas, C. Herzet E. Mémin. Wavelets and optic flow motion estimation. Numerical Mathematics: Theory, Methods and Applications, pp. 116-137, 6, 2013.
- P. Héas, C. Herzet, E. Mémin, D.Heitz, P.D. Mininni. Bayesian estimation of turbulent motion. IEEE transactions on Pattern Analysis And Machine Intelligence, 35(6), pp. 1343-56, June 2013.
- P. Héas, C. Herzet, E. Mémin. Bayesian inference of models and hyper-parameters for robust optic-flow estimation. IEEE transactions on Image Processing. 21(4):1437 -1451, 2012
- P. Héas, E. Mémin, D.Heitz, P.D. Mininni. Power laws and inverse motion modeling: application to turbulence measurements from satellite images. Tellus Series A: Dynamic Meteorology and Oceanography, 64, 10962, DOI: 10.3402/tellusa.v64i0.10962, 2012 .
- T. Corpetti, P. Héas, E. Mémin, N. Papadakis. Pressure image assimilation for atmospheric motion estimation. Tellus Series A: Dynamic Meteorology and Oceanography. 61(1):160-178, 2009.
- D. Heitz, P. Héas, E. Mémin, J. Carlier. Dynamic consistent correlation-variational approach for robust optical flow estimation. Experiments in Fluids. 45(4):595-608, 2008.
- P. Héas, E. Mémin. 3D motion estimation of atmospheric layers from image sequences . IEEE transactions on Geosciences and Remote Sensing, Vol. 46, Issue. 8,
pp. 2385-2396, 2008
- P. Héas, E. Mémin, N. Papadakis, A. Szantai. Layered estimation of atmospheric mesoscale dynamics from satellite imagery . IEEE transactions on Geosciences and Remote Sensing, pp. 4087-4104, vol. 45, Issue 12, Part 2, 2007
- P. Heas and M. Datcu, Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning,. IEEE Transactions on Geoscience and Remote Sensing, pp. 1635 - 1647, vol. 43, Issue 7, 2005 (IEEE GRS-S Transactions Prize Paper Award 06).
- M.Ciucu, P. Héas, M. Datcu et J. C. Tilton, Scale Space Exploration For Mining Image Information Content, Mining Multimedia and Complex Data, Revised Papers from KDD Workshop MDM/KDD 2002, Lecture Notes in Computer Science, Vol. 2797, pp. 118-133, Springer 2003.
National Journal
- P. Héas, D. Heitz, E. Mémin. La turbulence par l'image. La Recherche, 444, September 2010
International Conference Proceedings
- O. Hautecoeur, R. Borde, P. Héas, . Extraction of 3D Wind Profiles from Hyperspectral IASI Level 2 Products. 15th International Winds Workshop (IWW15), April 2021, Virtual.
- P. Héas, C. Herzet, B. Combès. Generalized Kernel-Based Dynamic Mode Decomposition. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, 2020. (Video Talk)
- P. Héas, C. Herzet, B. Combès. Learning Reduced Models with Kernel Methods. 14-ième colloque national en calcul des structures (CSMA19), Giens, France, May 2019
- O. Hautecoeur, R. Borde, P. Héas. 3D Winds derivation from infrared sounders. 14th International Winds Workshop (IWW14)
Maison Glad Jeju, Jeju City, South Korea, April 2018.
- P. Héas, C. Herzet. Low-rank Dynamic Mode Decomposition: Optimal Solution in Polynomial-time. SIAM Conference on Uncertainty Quantification, Los Angeles, USA, April 2018. .
- P. Héas, C. Herzet. Optimal Kernel-based Dynamic Mode Decomposition. SIAM Conference on Uncertainty Quantification, Los Angeles, USA, April 2018. .
- P. Héas, C. Herzet. Low-rank Dynamic Mode Decomposition: Optimal Solution in Polynomial-time. Model Reduction of Parametrized Systems IV (MoRePaS'18), Nantes, France, April 2018.
- P. Héas, C. Herzet. Optimal Kernel-based Dynamic Mode Decomposition. Model Reduction of Parametrized Systems IV (MoRePaS'18), Nantes, France, April 2018.
- C. Herzet, M. Diallo, P. Héas. Beyond Galerkin Projection by Using “Multi-Space” Priors. Model Reduction of Parametrized Systems IV (MoRePaS'18), Nantes, France, April 2018.
- C. Herzet, M. Diallo, P. Héas. Beyond Galerkin Projection by Using “Multi-Space” Priors. European Conference on Numerical Mathematics and Advanced Applications (ENUMATH), Voss, Norway, 2017.
- P. Héas, C. Herzet. Optimal Low-Rank Dynamic Mode Decomposition. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, USA, 2017.
- O. Hautecoeur, R. Borde, P. Héas. 3D winds derivation
from IASI level 2 products. 13th International Winds Workshop (IWW13)
Asilomar Conference Grounds, California, USA, June 2016.
- P. Héas, C. Herzet, Reduced-order Modelling of Hidden Dynamics , SIAM Conference on Uncertainty Quantification (UQ'16), Lausanne, Switzerland, April 2016.
- C. Herzet, P. Héas, Model Reduction from Partial Observations, Model-Order Reduction and Machine Learning Workshop (MORML'16), Stuttgart, Germany, March 2016.
- P. Héas, C. Herzet. Reduced-Order Modeling Of Hidden Dynamics. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASPP), Shanghai, China, 2016.
- A. Drémeau, P. Héas, C. Herzet. Sparse Representations in Nested Non-Linear Models, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASPP), Florence, Italy, 2014
- A. Drémeau, P. Héas, C. Herzet. Combining sparsity and dynamics: an efficient way, International Traveling Workshop on Interactions between Sprase models and Technology (ITWIST), Namur, Belgium, 2014
- P. Dérian, P. Héas, E. Mémin. Wavelets to reconstruct turbulence multifractals from experimental image sequences., In 7th Int. Symp. on Turbulence and Shear Flow Phenomena (TSFP), Ottawa, Canada, July 2011.
- P. Dérian, P. Héas, C. Herzet, E. Mémin. Wavelet-based fluid motion estimation., Scale Space Methods and Variational Methods (SSVM) in Computer Vision, Israel, June 2011.
- P. Héas, C. Herzet, E. Mémin. Robust optic-flow estimation with Bayesian inference of model and hyper-parameters, Scale Space Methods and Variational Methods (SSVM) in Computer Vision, Israel, June 2011.
- P. Dérian, P. Héas, E. Mémin, S. Mayor. Dense motion estimation from eye-safe aerosol lidar data., In International Laser Radar Conference, (ILRC), St Petersbourg, Russia, 2010.
- P. Héas, E. Mémin, D. Heitz, P. Mininni. Bayesian selection of scaling laws for motion modeling in images, International Conference on Computer Vision (ICCV), Kyoto, Japan, 2009.
- P. Héas, E. Mémin, D. Heitz, P. Mininni. Evidence of turbulence power laws from image data, Turbulent Mixing and Beyond (TMB), Trieste, Italy, 2009.
- P. Héas, E. Mémin. Inference on Gibbs optic flow prior : application to atmospheric turbulence characterization, IEEE International geoscience and remote sensing symposium (IGARSS), Cape town, South Africa, 2009.
- P. Héas, D. Heitz, E. Mémin. Multiscale regularization based on turbulent kinetic energy decay for PIV estimations with high spatial resolution, Symposium on Particle Image Velocimetry (PIV), Melbourne, Australia, 2009.
- P. Héas, E. Mémin, D. Heitz. Self-similar regularization of optic-flow for turbulent motion estimation, ECCV 1st International Workshop on Machine Learning for Vision-based Motion Analysis, Marseille, France, 2008.
- T. Corpetti, P. Héas, E. Mémin, N. Papadakis. Variational pressure image assimilation for atmospheric motion estimation, IEEE International geoscience and remote sensing symposium (IGARSS), Boston, USA, 2008
- P. Héas, E. Mémin. Optical-flow for 3D atmospheric motion estimation , 3rd International Conference on
Computer Vision Theory and Applications (VISAPP), Funchal, Portugal, 2008
- N. Papadakis, P. Heas and E. Mémin. Image assimilation for motion estimation of atmospheric layers with shallow-water model
. Asian Conference on Computer Vision (ACCV),
Tokyo, Japan, November 2007.
- D. Heitz, P. Héas, V. Navaza, J. Carlier, E. Mémin, Spatio-temporal correlation-variational approach for robust optical flow estimation. Symposium on Particle Image Velocimetry (PIV), Rome, Italy, September 2007.
- P. Héas, K Krissian, E. Mémin, A. Szantai.
Reconstruction and visualization of 3D wind fields from satellite image sequences.
15th Satellite Meteorology and Oceanography Conference of the American Meteorological Society, Amsterdam, Netherlands, September 2007.
- N. Papadakis, P. Heas and E. Mémin.
Motion estimation of 2D atmospheric layers with variational assimilation techniques.
15th Satellite Meteorology and Oceanography Conference of the American Meteorological Society, Amsterdam, Netherlands, September 2007.
- P. Héas, N. Papadakis and E. Mémin, A. Szantai.
Motion estimation of 2D atmospheric layers from satellite image sequences.
15th Satellite Meteorology and Oceanography Conference of the American Meteorological Society, Amsterdam, Netherlands, September 2007.
- P. Héas, E. Mémin, N. Papadakis. A consistent spatio-temporal motion estimator for atmospheric layers
. Scale Space Methods and Variational Methods (SSVM) in Computer Vision, LNCS 4485, pp 251 - 263, Ischia, Italy, May 2007.
- P. Héas, E. Mémin, N. Papadakis. Dense estimation of layer motions in
the atmosphere. International Conference on Pattern Recognition (ICPR),
Hong-Kong, August 2006.
- A. Szantai, P. Héas, E. Mémin, Comparison of atmospheric motion vectors and dense vector fields calculated from MSG images. 8th international winds workshop Beijing, China, 2006
- P. Heas and M. Datcu, Bayesian Learning on Graphs for Reasoning on Image Time-Series, Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt), Editors: R.Fisher, R.Preuss, U.V.Tousaint, American Institute of Physics, Vol. 735, pp. 127-137, 2004.
- P. Héas, M. Datcu, A. Giros et P.Marthon, Mining image time-series, IEEE International geoscience and remote sensing symposium(IGARSS), Anchorage, Alaska, pp. 2420 - 2423, vol.4, 2004.
- P. Héas, M. Datcu, A. Giros, Mining image time-series, Proceedings of ESA-EUSC 2004: Theory and Applications of Knowledge driven Image Information Mining, with focus on Earth Observation, ESA Special Publication no. 553, 2004.
- P. Héas, M. Datcu, A. Giros, P. Marthon, Image time series mining for dynamic scene understanding, IEEE International geoscience and remote sensing symposium (IGARSS), Toulouse, France, vol. 2, pp.1380 - 1382, 2003.
- P. Héas, M. Datcu, A.Giros, Trajectory of dynamic clusters in image time-series, Analysis of Multi-Temporal Remote Sensing Images, Proceedings of Multitemp 2003, Series in Remote Sensing, Vol. 3, pp 39-49, 2003.
- M.Ciucu, P. Héas, M. Datcu et J. C. Tilton, Scale Space Exploration For Mining Image Information Content, Proceeding of the Third International Workshop on Multimedia Data Mining / Knowledge Discovery and Data Mining (MDM/KDD), pp 30-38, 2002.
National Conference Proceedings
- D. Heitz, P. Héas, C. Herzet. Incertitudes de mesures PIV par flot optique.,14eme Congres Francais de Visualisation d'Images en Mecanique des Fluides, Lille, 2011.
- D. Heitz, P. Héas, V. Navaza, J. Carlier, E. Mémin, Collaboration correlation-variationnelle pour une estimation robuste du flot-optique, 18ème Congrès Français de Mecanique (CFM), 2007.
Thesis
- P. Héas, Apprentissage bayesien de structures spatio-temporelles : application a la fouille d'information dans les series temporelles d'images satellites, Ph.D. Thesis of SUPAERO, Toulouse, No 413; April 2005
- P. Héas, Segmentation d'images astronomiques par morphologie mathematique, Master Thesis of SUPAERO, Toulouse; July 2001
Misc
- P. Héas, E. Mémin, D. Heitz, P. Mininni. Turbulence power laws and inverse motion modeling in images. INRIA research report - 6948 ,2009.
- T. Corpetti, P. Héas, E. Mémin, N. Papadakis. Pressure image assimilation for atmospheric motion estimation. INRIA research report - 6507,2008.
- P. Héas, N. Papadakis, E. Mémin, Time-consistent estimators of 2D/3D motion of atmospheric layers from pressure images, INRIA research report - 6292, 2007.
- P. Héas, M. Datcu, Supervised learning on graphs of spatio-temporal similarity in satellite image sequences, INRIA research report - 6299, 2007.
