Computer Vision & Pattern Recognition
The purpose of the course is to introduce basic problems in image processing, computer vision, and patter recognition, and to provide the students with an understanding of fundamental principles underlying the most important solutions. Topics include: image formation (from both a photometric and geometric perspective), low-level imaging methods (filtering and edge detection), image restoration and inverse problems (in particular denoising), single and multi-view geometry for 3D reconstruction, feature extraction for object recognition, 3D surfaces and their registration. Lectures are accompanied by various examples of applications where these methods apply, and hands-on programming exercise to solve real-world problems. Prerequisites are linear algebra, basic probability and statistics.
Master of Science in Computational Science, Elective course, Lecture, 1st year
Master of Science in Computational Science, Elective course, Lecture, 2nd year
Master of Science in Informatics, Elective course, Lecture, 1st year
Master of Science in Informatics, Elective course, Lecture, 2nd year