EU Regional School - Pock Seminar
Prof. Dr. Thomas Pock - Variational Methods for Computer Vision: Modeling, Numerical Solution and Learning
Institute for Computer Graphics and Vision, Graz University of Technology, Austria
Abstract
Variational methods (also known as energy minimization methods) are among the
most flexible methods for solving inverse problems. The idea is to set up an
energy functional whose low energy states correspond to physically plausible
solutions of the problem. Hence, computing the solution of a problem is
formulated as an optimization problem. In this course, you will learn about
variational methods for solving classical computer vision problems such as image
restoration, image segmentation, stereo and motion estimation. You will learn
about both the basic modeling aspects (different regularization terms and data
fitting terms) as well as numerical optimization algorithms to solve the models.
Moreover, you will learn about functional lifting, which is a technique whose
aim is to reformulate a hard problem (usually due to non-convexity) in a higher
dimensional space, where the problem becomes convex. Finally, you will also
learn about our recent activities to improve variational models by means of
machine learning techniques.