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Computer Vision & Pattern Recognition


Hormann K.

Course director

Zimmerman N.



The course covers the following topics: image formation (from both a photometric and geometric perspective), low-level imaging methods (filtering and edge detection), single and multi-view geometry for 3D reconstruction, feature extraction for object recognition. Lectures are accompanied by various examples of applications where these methods apply, and hands-on programming exercise to solve real-world problems.


The purpose of the course is to introduce basic problems in image processing, computer vision, and pattern recognition, and to provide the students with an understanding of fundamental principles underlying the most important solutions.

Teaching mode

In presence

Learning methods

The topics will be presented in the form of lectures and tutorials. Homework assignments with theoretical and practical programming exercises will be handed out, partially graded, and discussed in the tutorials.

Examination information

The course grade is determined by the results of the homework assignments (20%) and the written final exam (80%).