Variational methods and effective algorithms for imaging and vision
| Created: | 2017-08-31 09:05 |
|---|---|
| Institution: | Isaac Newton Institute for Mathematical Sciences |
| Description: | Programme
29th August 2017 to 20th December 2017 Organisers: Ke Chen (University of Liverpool), Andrew Fitzgibbon (Microsoft Research), Michael Hintermüller (Humboldt-Universität zu Berlin), Carola-Bibiane Schönlieb (University of Cambridge), and Xue-Cheng Tai (Hong Kong Baptist University) Scientific committee: Andrea Bertozzi (UCLA, USA); Andrew Blake (Alan Turing Institute, UK); Tony Chan (HKUST, CHINA); Bill Freeman (MIT, USA); Ron Kimmel (Technion, Israel); David Mumford (Brown, USA); Mila Nikolova (E.N.S. Cachan, France); Stanley Osher (UCLA, USA); Joachim Weickert (Saarland, Germany). Programme Theme In our modern society, mathematical imaging, image processing and computer vision have become fundamental for gaining information on various aspects in medicine, the sciences, and technology, in the public and private sector equally. The rapid development of new imaging hardware, the advance in medical imaging, the advent of multi-sensor data fusion and multimodal imaging, as well as the advances in computer vision have sparked numerous research endeavours leading to highly sophisticated and rigorous mathematical models and theories. An evidence of this trend can be found in the still increasing use of variational models, shapes and flows, differential geometry, optimization theory, numerical analysis, statistical / Bayesian graphical models, and machine learning. Still, the ever growing challenges in applications and technology constantly generate new demands that cannot be met by existing mathematical concepts and algorithms. As a consequence, new mathematical models have to be found, analyzed and realized in practice. This four-month programme will foster exchange between different groups of researchers and practitioners, who are involved in mathematical imaging science, and discussions on new horizons in theory, numerical methods and applications of mathematical imaging and vision. |
Media items
This collection contains 94 media items.
Media items
A convexity based method for approximation and interpolation of sampled functions
Zhang, K( University of Nottingham)
Wednesday 8th November 2017 - 15:30 to 16:30
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 5 Jan 2018
A Nuclear-norm Model for Multi-Frame Super-resolution Reconstruction
Chan, R
Friday 3rd November 2017 - 11:10 to 12:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 6 Nov 2017
Accelerated Free-Form Model Discovery of Interpretable Models using Small Data
Horesh, L
Tuesday 31st October 2017 - 11:10 to 12:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 2 Nov 2017
Adaptive and Move Making Auxiliary Cuts for Binary Pairwise Energies
Veksler, O
Friday 8th September 2017 - 12:00 to 12:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 11 Sep 2017
Advancements in Hybrid Iterative Methods for Inverse Problems
Chung, J
Tuesday 31st October 2017 - 16:30 to 17:20
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 2 Nov 2017
Alternating projections for phase retrieval with random sensing vectors
Waldspurger,I
Friday 3rd November 2017 - 09:00 to 09:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 6 Nov 2017
Alternating proximal gradient descent for nonconvex regularised problems with multiconvex coupling terms
Nikolova, M
Friday 8th September 2017 - 09:00 to 09:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 14 Sep 2017
An interpolating distance between Wasserstein and Fisher-Rao
Vialard, F-X (Université Paris-Dauphine, INRIA Paris - Rocquencourt)
Friday 15th December 2017 - 11:30 to 12:30
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 15 Dec 2017
Analysis and applications of structural-prior-based total variation regularization for inverse problems
Holler, M
Friday 3rd November 2017 - 09:50 to 10:40
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 6 Nov 2017
Augmented Lagrangian method for image segmentation using elastica energy that prefers convex contours
Tai, X
Thursday 7th September 2017 - 09:00 to 09:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 8 Sep 2017
Automating stochastic gradient methods with adaptive batch sizes
Goldstein, T
Wednesday 6th September 2017 - 09:50 to 10:40
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 7 Sep 2017
Bayesian analysis and computation for convex inverse problems: theory, methods, and algorithms
Pereyra, M
Thursday 2nd November 2017 - 14:50 to 15:40
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 3 Nov 2017
Below the Surface of the Non-Local Bayesian Image Denoising Method
Nikolova, M
Wednesday 1st November 2017 - 09:00 to 09:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Thu 2 Nov 2017
Breaking the Curse of Dimensionality with Convex Neural Networks
Bach, F
Tuesday 31st October 2017 - 14:50 to 15:40
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 3 Nov 2017
Cancer ID - From Spectral Segmentation to Deep Learning
Brune, C
Monday 30th October 2017 - 12:00 to 12:50
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Tue 31 Oct 2017
Capturing 3D models of deformable objects from monocular sequences
Agapito, L (University College London)
Tuesday 12th December 2017 - 11:30 to 12:30
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 15 Dec 2017
Cell detection by functional inverse diffusion and group sparsity
del Aguila Pla, P
Thursday 2nd November 2017 - 15:40 to 16:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 6 Nov 2017
Compact Rank Models and Optimization
Olsson, C (Lund University, Chalmers University of Technology)
Tuesday 12th December 2017 - 16:00 to 17:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 15 Dec 2017
Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function
Crooks, E (Swansea University)
Friday 15th December 2017 - 10:00 to 11:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 15 Dec 2017
Convex regularization of discrete-valued inverse problems
Clason, C
Thursday 7th September 2017 - 16:10 to 17:00
Collection: Variational methods and effective algorithms for imaging and vision
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 8 Sep 2017