Dr. Bernhard Burgeth
(Bld. 27.1, room 409, phone 0681-302-64382,
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.
In this course we will discuss
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 and principal component analysis used for data compression, for example.
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.
The lectures are suited for students of mathematics and computer science. Undergraduate level knowledge of mathematics is required. The necessary knowledge from image processing and statistics/probability theory will be provided during classes.
Time: Wednesday 11:00–13:00 (11 a.m. –1 p.m.)
Location: Bld. 45, Lecture hall 003
The slides are available for participants of the classes. (access is password-protected):
|Section 2||Elements of Probability Theory I|
|Section 3||Elements of Probability Theory II|
|Section 4||Elements of Probability Theory III|
|Section 5||Elements of Probability Theory IV|
|Section 6||Elements of Probability Theory V|
|Section 7||Histogram Based Operations on Images I|
|Section 8||Histogram Based Operations on Images II|
|Section 9||Histogram Based Operations on Images III|
|Section 10||Registration by Maximisation of Mutual Information|
|Section 11||Estimation in Statistics: Parzen and ML|
|Section 12||PCA, POD, KLT via SVD|