After successful completion of the course, students are able to utilize methods that understand the content of a digital image in order to produce a human-interpretable description or to execute concrete actions as, i.e., the steering of an autonomous car.

Advanced image analysis techniques from image acquisition to complex scene interpretation, including - Human visual system - Image formation - Mathematical Morphology - image segmentation - Colour image analysis - Texture analysis - Object recognition

Inverted class room model: the basic material of the lecture is distributed to the participating students. Each one presents one particular topic online to the others. Opponents (= another student) introduce the following discussion of all participants. The main resulting statements are documented by a third student.

Books:

R. Szeliski. Computer Vision, Springer 2011.

M. Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis and Machine Vision (2nd Edition), PWS Publishing, 1999

P. Soille: Morphological Image Analysis - Principles and Applications (2nd Edition), Springer, 2004

R. Klette: Concise Computer Vision, Springer 2014.

Plenary online presentation of the selected topic together with a short written summary.
The critical questions asked by the opponent, and the written report of the discussion.

Foundations of digital image analysis