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Variational Methods for Computer Vision
WS 2013/14, TU München
Lecture
Location: Room 02.09.023
Time and Date:
Wednesday, 10.15h - 11.45h
Thursday, 10.15h - 11.45h
Lecturer: Prof. Dr. Daniel Cremers
Start: Thursday 17.10.2013
The lectures will be held in English, if desired.
Exercises
Location: Room TBA
Time and Date:
TBA
Organization: Dipl. Inf. Mohamed Souiai
Start:TBA
Summary
Variational Methods are among the most classical techniques for optimization of cost functions in higher dimension. Many challenges in Computer Vision and in other domains of research can be formulated as variational methods. Examples include denoising, deblurring, image segmentation, tracking, optical flow estimation, depth estimation from stereo images or 3D reconstruction from multiple views.
In this class, I will introduce the basic concepts of variational methods, the Euler-Lagrange calculus and partial differential equations. I will discuss how respective computer vision and image analysis challenges can be cast as variational problems and how they can be efficiently solved. Towards the end of the class, I will discuss convex formulations and convex relaxations which allow to compute optimal or near-optimal solutions in the variational setting.
Prerequisites
The requirements for the class are knowledge in basic mathematics, in particular multivariate analysis and linear algebra. Some prior knowledge on optimization is a plus but is not necessary.