Computer Vision I: Variational Methods
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2015/6 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2015/6||SS 2015||WS 2011/2|
IN2246 is a semester module in English language at Master’s level which is offered irregular.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Many challenges in Computer Vision and in other domains of research can be formulated as variational methods.
Exemples include denoising, deblurring, image segmentation, tracking, optical flow estimation, depth estimation from stereo images or 3D reconstruction from multiple views.
In this class, the basic concepts of variational methods will be introduced :
- The Euler-Lagrange calculus and partial differential equations
- Formulation of computer vision and image analysis challenges as variational problems
- Efficient solution of variational problems
- Discussion of convex formulations and convex relaxations to compute optimal or near-optimal solutions in the variational setting
The key concepts will be implemented in Matlab to provide hands-on experience.
They are able to efficiently solve variational problems and to implement the solution with Matlab.
MA0902 Analysis for Informatics
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VU||6||Variational Methods for Computer Vision (IN2246)|
Learning and Teaching Methods
Description of exams and course work
The exam may be repeated at the end of the semester.