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M15

M15: Inverse Problems in Computer Vision 

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. Technically, imaging and vision are concerned with the computation, visualisation and the automatic processing of (digital) images.Such images may come from consumers’ digital cameras, from industrial installations, as well asimages resulting from visualizing (material or tissue) properties, which are accessible only indirectly through measurements. The term computer vision encompasses methods for analysing and understanding images, for example recovering a representation of the 3D world from a set of 2D images, or partitioning the image domain into regions corresponding to known object categories. The replication of the skills of human vision by electronically perceiving and understanding an image has been a central theme since the inception of this scientific field. 

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, differential geometry, optimization theory, numerical analysis, statistical / Bayesian graphical models, and machine learning. Still, the evergrowing 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.

We want to bring together experts and young researches working in this field to discuss about the analysis and numerics of inverse problems in computer vision.

Organizer:
Carola-Bibiane Schönlieb, University of Cambridge, UK,  This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers

Clemens Kirisits, University of Vienna, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Convective regularization for optical flow

Lavdie Rada, Bahcesehir University, Turkey, This email address is being protected from spambots. You need JavaScript enabled to view it.
An improved model for joint segmentation and registration based on linear curvature smoother

Talal Rahman, Bergen University College, Norway, This email address is being protected from spambots. You need JavaScript enabled to view it.
A new versatile variational model for surface reconstruction

Yves van Gennip, University of Nottingham, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.
Graph based techniques for image segmentation

Anne-Sophie Macé, Paris Descartes & Bioaxial, France, This email address is being protected from spambots. You need JavaScript enabled to view it.
Robust reconstruction of sparse solutions of ill-posed inverse problems with applications to super-resolution microscopy

Xiahao Cai, University College London, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.
Wavelet-based segmentation on the sphere

Carolin Rossmanith, University of Münster, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optimal transportation networks as Mumford-Shah-type optimisation problems

Hendrik Dirks, University of Münster, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optical-flow based dynamic imaging methods

Luca Calatroni, Università degli Studi di Genova, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.
A novel total variation denoising model for mixed noise removal