Collection 2019 - T1 - WS2 - Statistical Modeling for Shapes and Imaging
Appears in collection : 2019 - T1 - The Mathematics of Imaging
Date(s) 11/03/2019 - 15/03/2019
Mathematics
and Interactions
Date(s) 11/03/2019 - 15/03/2019
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All the collection videos (25)
![Synthesizing stochastic microstructures for additive manufacturing]()
48:51published on April 16, 2019Synthesizing stochastic microstructures for additive manufacturing
By Sylvain Lefèbvre
![Detecting Overfitting of Deep Generative Networks via Latent Recovery]()
38:40published on April 16, 2019Detecting Overfitting of Deep Generative Networks via Latent Recovery
By Julien Rabin
![Interaction between invariant structures for shape analysis]()
46:52published on April 16, 2019Interaction between invariant structures for shape analysis
By Ron Kimmel
![3D Point Cloud Classification, Segmentation and Normal estimation, using 3D Modified Fisher Vector Representation and Convolutional Neural Networks]()
46:27published on April 16, 20193D Point Cloud Classification, Segmentation and Normal estimation, using 3D Modified Fisher Vector Representation and Convolutional Neural Networks
By Michael Lindenbaum
![Total variation denoising with iterated conditional expectation]()
42:55published on April 16, 2019Total variation denoising with iterated conditional expectation
By Cécile Louchet
![Hybrid sparse stochastic processes and the resolution of linear inverse problems]()
50:44published on April 16, 2019Hybrid sparse stochastic processes and the resolution of linear inverse problems
By Michael Unser
![Lipschitz-Killing curvatures of excursion sets for 2D random fields]()
41:28published on April 16, 2019Lipschitz-Killing curvatures of excursion sets for 2D random fields
By Hermine Biermé
![Efficient sampling through variable splitting-inspired bayesian hierarchical models]()
45:08published on April 16, 2019Efficient sampling through variable splitting-inspired bayesian hierarchical models
By Pierre Chainais
![Statistical aspects of stochastic algorithms for entropic optimal transportation between probability measures]()
43:32published on April 16, 2019Statistical aspects of stochastic algorithms for entropic optimal transportation between probability measures
By Jérémie Bigot
![Stochastic Approximation-based algorithms, when the Monte Carlo bias does not vanish]()
40:46published on April 16, 2019Stochastic Approximation-based algorithms, when the Monte Carlo bias does not vanish
By Gersende Fort
![PDEs on the Homogeneous Space of Positions and Orientations]()
57:14published on April 16, 2019PDEs on the Homogeneous Space of Positions and Orientations
By Remco Duits
![Mixed-effect model for the spatiotemporal analysis of longitudinal manifold-valued data]()
41:00published on April 16, 2019Mixed-effect model for the spatiotemporal analysis of longitudinal manifold-valued data
By Stéphanie Allassonnière
![A fast statistical colocalization method for 3D live cell imaging and super-resolution microscopy]()
42:17published on April 16, 2019A fast statistical colocalization method for 3D live cell imaging and super-resolution microscopy
By Charles Kervrann
![Multiple objects detection in biological images using a Marked Point Process Framework]()
50:47published on April 16, 2019Multiple objects detection in biological images using a Marked Point Process Framework
By Xavier Descombes
![Création des mondes virtuels : Objets auto-similaires et distributions d'éléments]()
01:05:57published on April 16, 2019Création des mondes virtuels : Objets auto-similaires et distributions d'éléments
By Marie-Paule Cani
![From currents to oriented varifolds for data fidelity metrics; growth models for computational anatomy]()
49:32published on April 16, 2019From currents to oriented varifolds for data fidelity metrics; growth models for computational anatomy
By Irène Kaltenmark
![Kernel norms on normal cycles and the KeOps library for linear memory reductions over datasets]()
49:04published on April 16, 2019Kernel norms on normal cycles and the KeOps library for linear memory reductions over datasets
By Joan Glaunès
![Bayesian inference and convex geometry: theory, methods, and algorithms]()
49:33published on April 16, 2019Bayesian inference and convex geometry: theory, methods, and algorithms
By Marcelo Pereyra
![Modular large deformation and shape aware metrics in shape analysis: How to make things simple (and meaningful)?]()
55:38published on April 16, 2019Modular large deformation and shape aware metrics in shape analysis: How to make things simple (and meaningful)?
By Alain Trouvé
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