252-0527-00L  Probabilistic Graphical Models for Image Analysis

SemesterAutumn Semester 2014
LecturersB. V. McWilliams
Periodicityyearly recurring course
Language of instructionEnglish

Catalogue data

AbstractThis course will focus on the algorithms for inference and learning with statistical models. We use a framework called probabilistic graphical models which include Bayesian Networks and Markov Random Fields.

We will use examples from traditional vision problems such as image registration and image segmentation, as well as recent problems such as object recognition.
ObjectiveStudents will be introduced to probablistic graphical models and will learn how to apply them to problems in image analysis and understanding. The focus will be to study various algorithms for inference and parameter learning.
LiteratureWill be announced during the lecture.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersB. V. McWilliams
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationoral 30 minutes
Additional information on mode of examination20 Minuten Prüfung, 10 Minuten Besprechung
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


252-0527-00 GProbabilistic Graphical Models for Image Analysis3 hrs
Mon15-16CAB G 51 »
Thu10-12CLA E 4 »
02.02.15-17CAB G 56 »
B. V. McWilliams


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Certificate of Advanced Studies in Computer ScienceFocus Courses and ElectivesWInformation
Computer Science MasterFocus Elective Courses Visual ComputingWInformation
Computational Science and Engineering MasterElectivesWInformation
Robotics, Systems and Control MasterPerception, Graphics and Virtual RealityWInformation