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Computer Vision I: Variational Methods (IN2246)

Course 0000002737 in WS 2018/9

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 6 SWS
Organisational Unit Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers)
Lecturers Daniel Cremers
Björn Häfner
Marvin Eisenberger
David Schubert
Dates Wed, 10:30–12:30, Interims II 004
Thu, 10:00–12:00, Interims I 102
Tue, 10:00–12:00, Interims I 102

Assignment to Modules

This course is not assigned to any module.

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks 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.
Links TUMonline entry

Equivalent Courses (e. g. in other semesters)

WS 2019/20 Computer Vision I: Variational Methods (IN2246) Cremers, D. Demmel, N.
Assistants: Maier, R.
Wed, 10:30–12:30, Interims II 004
Tue, 10:00–12:00, Interims I 102
Thu, 10:00–12:00, Interims I 102
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