Welcome to the homepage of the lecture Probabilistic Methods in Image Analysis Winter term 2008/09 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Home |
Lectures (3h) with exercises and assignments (1h);
6 ECTS points
Prerequisites – Synopsis – Tutorials – Exams – Course Material – Literature This course is suitable for students of mathematics , physics or computer science who completed their undergraduate studies in mathematics. Knowledge of probablity theory or statistics is helpful but not required. The lectures will be given in English. Hence passive knowledge of English is necessary.
Probabilistic techniques are employed quite successfully
in the processing and analysis of images, however, they also
play a vital role in pattern classification,
data mining and learning theory.
- basic notions from probability theory and statistics as well as from image processing
- histogram based image analysis and enhancement methods
- the probabilistic background of the Karhunen-Loeve expansion used for data compression, for example
- independent component analysis and applications
- the notion of entropy in image registration
- and, if time permits, we will give an introduction to the basic ideas of Markov random fields and simulated annealing.
On Fridays, 9:15 -- 10:00
There will be an opportunity for an oral exam at the end of the lecture period, Friday, February 13th,
and a second oral exam at the end of the semester.
Active participation in the in the exercises is expected in order to be admitted
to the exams.
Participants of the course can download the course material
(access is password-protected).
Relevant references will be provided in the lecture. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

MIA Group |