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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

Home Teaching Summer Semester 2019 Probabilistic Graphical Models in Computer Vision (IN2329)(2h + 2h, 5 ECTS)

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


There will be NO lecture on Wednesday, 24.04.2019.

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.

We will cover the following topics.

- Graphical model representations:

  • Bayesian network
  • Markov network
  • Conditional random field
  • Factor graph
  • Exponential family

- Graphical model inference:

  • Variable elimination
  • Sum-product algorithm
  • Junction-tree algorithm
  • Graph cut and move-making extensions
  • Linear programming relaxation

- Approximative inference techniques:

  • Loopy belief propagation, message passing
  • Mean field, variational inference
  • Sampling methods

- Graphical model learning:

  • Maximum-likelihood estimation
  • Expectation-maximization algorithm
  • Structured SVM

- Further topics if time allows

We will implement some of them in Python.


Location: Room 02.09.014
Time and Date: Monday 16:15 - 18:00
Start: April 29th, 2019
Lecturer: Dr. Tao Wu
The lecture is held in English.


Location: Room 02.09.023
Time and Date: Wednesday 12:15 - 14:00
Start: May 08th, 2019
Organization: Yuesong Shen, Zhenzhang Ye

The exercise sheets consist of two parts, theoretical and programming exercises.

Exercise sheets will be posted every Monday and are due a week later. You will have one week to do the exercises.
Please submit the programming solutions as a zip file with filename "matriculationnumber_firstname_lastname.zip" only! containing your code-files (no material files) via email to pgm-ss19@vision.in.tum.de, and hand in the solutions to the theoretical part in Monday's lecture. We will give you back the corrected sheets on Wednesday when we discuss them in class.
Please remember to write clean, commented(!) code! You are allowed to work on the exercise sheets in groups of two students.

The exercise sheets can be accessed here.

Exam Bonus

To achieve the bonus, you have to meet two requirements:
1. get at least 75% grades of all exercises totally, i.e. sum of the grades you get in all exercises divided by the grades of all exercises (without bonus) should be >= 0.75
2. present your theoretical solution during tutorial at least once in this semester.
You cannot improve either 1.0 or >4.0.



Lecture Materials

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

Send us an email if you need the password.

Rechte Seite

Informatik IX
Chair for Computer Vision & Artificial Intelligence

Boltzmannstrasse 3
85748 Garching