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

Technical University of Munich

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

Summary

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
Prerequisites

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 programming skills (eg., C/C++, Matlab) and algorithms and data structures (eg., dynamic programming).

Exam

Please do not forget to register to the exam in the TUM online.

The exam will be oral.

The time slots for the exams are announced on the internal page.

Lectures

Location: Room 02.09.023
Time and Date: Monday 10:15 - 12:00 (changed)
Lecturer: Dr. Csaba Domokos
Start: 24 April 2017

The lectures are held in English.

Tutorials

Location: Room 02.05.014
Time and Date: Monday 8:15 - 10:00 (changed)
Start: 24 April 2017

Date of Tutorial Exercise Sheet
April, 24th Exercise sheet 1
May, 8th Exercise sheet 2
May, 15th Exercise sheet 3
May, 22nd Exercise sheet 4
May, 29th Exercise sheet 5
June, 12th Exercise sheet 6
June, 19th Exercise sheet 7
June, 26th Exercise sheet 8
July, 3rd Exercise sheet 9
July, 10th Exercise sheet 10
July, 17th Exercise sheet 11
July, 31st Exercise sheet 12
Internal page

Solution sheets and extra announcements can be accessed here.

The password will be presented in the first lecture.

  • 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.

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Informatik IX
Computer Vision Group

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

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