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Variational Methods for Computer Vision

WS 2011/12, TU M√ľnchen

Lecture

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.

Exercises

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

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.

Lecture Material

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

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

Last edited 30.10.2013 12:31 by Mohamed Souiai