Variational Methods and Effective Algorithms for Imaging and Vision Programme at the Isaac Newton Institute

Institute director Carola Schonlieb, along with Ke Chen (University of Liverpool), Andrew Fitzgibbon (Microsoft Research), Michael Hintermüller (Humboldt-Universität zu Berlin) and Xue-Cheng Tai (Universitetet i Bergen) will be hosting a programme on Variational Methods and Effective Algorithms for Imaging and Vision at the Isaac Newton Institute.  The programme will run from 29th August 2017 to 20th December 2017, and will include three workshops on ‘Variational methods, new optimisation techniques and new fast numerical algorithm’, ‘Generative models, parameter learning and sparsity’ and ‘Flows, mappings and shapes’.

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.

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