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Variational methods and effective algorithms for imaging and visionEpisode from the podcastVariational methods and effective algorithms for imaging and vision
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Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Released Friday, 15th December 2017
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Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Compensated convexity, multiscale medial axis maps, and sharp regularity of the squared distance function

Friday, 15th December 2017
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Crooks, E (Swansea University)Friday 15th December 2017 - 10:00 to 11:00

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Programme29th August 2017 to 20th December 2017Organisers: 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 ThemeIn 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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