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Home Teaching Summer Semester 2017 Probabilistic Graphical Models in Computer Vision (IN2329) (2h + 2h, 5 ECTS)

Probabilistic Graphical Models in Computer Vision (IN2329) (2h + 2h, 5 ECTS)


Several problems in computer vision can be cast as a labeling problem. Typically, such problems arise from Markov Random Field (MRF) models, which provide an elegant framework of formulating various types of labeling problems in imaging.

By making use of certain assumptions some „nice“ MRF models can be solved in polynomial time, whereas others are NP hard. We will see both, efficient algorithms for solving the „nice“ problems and relaxation strategies for the „hard“ ones.

The following topics will be covered in this module:

Directed and undirected graphical models

  • Bayesian network
  • Markov random field
  • Conditional random field

Parameter learning for MRF and CRF models

  • Gradient based optimization
  • Stochastic gradient descent
  • Structured support vector machine

Exact MAP inference methods for MRFs

  • Belief propagation on trees: Max-sum algorithm
  • Binary graph cuts
  • Branch-and-mincut

Approximate inference methods

  • Loopy belief propagation
  • Mean field approximation
  • Graph cuts: alpha expansion, alpha-beta swap
  • Linear programming relaxations: fast primal-dual schema

Practical applications that we will cover are:

  • Binary and multi-label image segmentation
  • Human pose estimation
  • Stereo matching
  • Object detection


Location: Room 02.09.023
Time and Date: Monday 10:00 - 12:00
Lecturer: Dr. Csaba Domokos
Start: 24 April 2017

The lectures are held in English.


Location: Room 02.05.014
Time and Date: Monday 14:00 - 16:00
Start: 24 April 2017


The course is intended for Master students.

The requirements for the class are knowledge in basic mathematics, in particular multivariate analysis and linear algebra, and in basic computer science, in particular dynamic programming and basic data structures.


The exam will be oral.

  • D. Koller, N. Friedman. Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
  • S. Nowozin, C. H. Lampert. Structured Learning and Prediction in Computer Vision, Foundations and Trends in Computer Graphics and Vision, 2011. Download
  • A. Blake, P. Kohli, C. Rother. Markov Random Fields for Vision and Image Processing, MIT Press, 2011.
Lecture Material

Course material (slides and exercise sheets) can be accessed here.

The password will be presented in the first lecture.

Last edited 28.02.2017 00:01 by Csaba Domokos