Advanced Variational Methods
Summer Term 2016
Advanced Variational Methods in
25/07/2016: Schedule for the second on 20/10/2016 is online.
Many problems in image processing, computer vision, and machine learning can be formulated
as a variational model. Variational models allow for a clean formulation of the problem without
hidden features. Moreover, usually they are amenable to efficient optimization techniques.
Although we will also consider how to efficiently optimize the models, the focus
of this lecture is the modelling of the problems. Modelling and optimization must be considered
together. A perfect model that cannot be solved is as bad as a too simple model that can
be solved without any computation. Key for the modelling with variational models is the
trade-off between accuracy in modelling and the solvability.
Prerequisites: Basic mathematics (such as Mathematik für Informatiker I-III, or calculus and linear algebra). Knowledge in image processing and computer vision is helpful, but not required. Understanding English is necessary.
The tutorials include practical and theoretical classroom assignments. Attendance of the tutorials is not mandatory, but highly recommended.
In order to register for the Lecture, write an e-mail to Peter Ochs. The subject line must begin with the tag [AVIC16]. Please use the following template for the e-mail:
First name: [myFirstName]
Note that the e-mail address from which you send this information will be used to provide you with urgent information concerning the lecture.
This registration is for internal purposes at our chair only and completely independent of any System like LSF/Hispos. They require a separate registration.
First exam: 28. July 2016
Registration for the second exam:
Time schedule for the second exam: (20. October 2016)
Participants of the course can download the lecture materials here after the lecture
(access is password-protected). However, be aware that these slides are only
provided to support the classroom teaching, not to replace it. Additional
organisational information, such as examples and explanations that may be
helpful or necessary to understand the content of the course (and thus
relevant for the exam), will be provided in the lectures. It is solely
your responsibility - not ours - to make sure that you receive this
References will be given during the lecture.