Direkt zum Inhalt springen
Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

Home Teaching Winter Semester 2011/12 Variational Methods for Computer Vision

Variational Methods for Computer Vision

WS 2011/12, TU München


Location: Room 02.09.023
Time and Date: Monday 10.15h - 11:45h, Wednesday, 11.15h - 12.00h
Lecturer: Prof. Dr. Daniel Cremers
Start: Wednesday, 19.10.2011

The lectures will be held in English, if desired.


Location: Room 00.07.037
Time and Date: Friday 12.15h - 14:30h
Organization: Dipl. Inf. Mohamed Souiai
Start: Friday, 04.11.2011


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.


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.

Lecture Material

Course material (slides and exercise sheets) can be downloaded here.

Please email Dipl. Inf. Mohamed Souiai for password request.

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching