Location: Room 02.09.023
Time and Date:
Wednesday, 10.15h - 11.45h
Thursday, 10.15h - 11.45h
Lecturer: Prof. Dr. Daniel Cremers

The lectures are held in English.

The retry exam will take place on the 29.03.2016 at 14.30-16.30 in room 02.09.023.

Location: 02.05.014
Time and Date: Wednesday, 16.00h - 18.15h
Organization: Mohamed Souiai, Georg Kuschk

Location: to be announced
Time and Date: Friday, Jan 29th, 16.00h - 18.15h

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.

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

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