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TUM School of Computation, Information and Technology
Technical University of Munich

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Variational Methods for Computer Vision

Tutorial ICCV 2011

Daniel Cremers, Bastian Goldlücke, Thomas Pock

Content

  • Basic concepts of variational methods: variational approach, Euler-Lagrange equations, duality, …
  • Foundations of convex optimization and convex relaxation techniques
  • Optimal first-order algorithms for convex optimization
  • Variational formulations for classical computer vision problems: image segmentation, optical flow, stereo and multiview reconstruction, …
  • Relations to discrete optimization and Markov random fields
  • Mumford-Shah and multilabel problems
  • Numerical implementation and GPU acceleration

Schedule & Slides

Sunday, Nov. 6, Full day

9:30 - 10:15Mathematical FoundationsBastian Goldlücke
Coffee break
10:35 - 11:20Continuous Optimization in Computer VisionThomas Pock
Coffee break
11:40 - 12:25Variational Methods and Geometric ReconstructionDaniel Cremers
Lunch break
14:00 - 14:45Convex Relaxation for Motion and StereoThomas Pock
Coffee break
15:05 - 15:50Convex Relaxations for Multi-label ProblemsDaniel Cremers
Coffee break
16:10 - 16:55Vectorial Multilabel ProblemsBastian Goldlücke

Complete Slide Download

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News

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023.

31.08.2022

Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.

17.07.2022

MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.

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