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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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Computer Vision I: Variational Methods

WS 2019/20, TU München

News

31.01.20:

  • There will be a Q&A session in the exercise on Wednesday, 05.02.2020.
Lecture

Location: Interims Hörsaal 2 (5620.01.102)
Time and Date:
Tuesday, 10.15h - 11.45h
Thursday, 10.15h - 11.00h
Lecturer: Prof. Dr. Daniel Cremers

The lectures are held in English.

Exercises

Location: Interims II (at the chemistry building): 004, Hörsaal 1 (5416.01.004)
Time and Date: Wednesday, 10.30h - 12.30h
Organization: Marvin Eisenberger and Emanuel Laude
Contact: cvvm-ws19@vision.in.tum.de

Exam

Location: 00.02.001, MI HS 1, Friedrich L. Bauer Hörsaal (5602.EG.001)
Time and Date: 26.02.2020, 10.30h - 12.30h
You may only use standard writing materials. No cheat sheet, no electronic devices.
Exam review: tbd.

Retake exam

Location: tbd.
Time and Date: 15.04.2020, 10.30h - 12.30h
You may only use standard writing materials. No cheat sheet, no electronic devices.
Exam review: tbd.

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

Slides and exercise sheets can be accessed here.

Videos

A previous (very similar) version of this course was recorded in 2013. The videos can be found on Youtube.

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More