Computer Vision



Time and place:

  • Mon 8:15-9:45, Room H4

Fields of study

  • WPF INF-MA from SEM 1
  • WF ICT-MA-MPS from SEM 1
  • WF CME-MA from SEM 1
  • WPF AI-MA from SEM 1


This lecture discusses important algorithms from the field of computer vision. The emphasis lies on 3-D vision algorithms, covering the geometric foundations of computer vision, and central algorithms such as stereo vision, structure from motion, optical flow, and 3-D multiview reconstruction. The course will also introduce Convolutional Neural Networks (with some examples to play around) and discuss it's importance and impact. Participants of this advanced course are expected to bring experience from prior lectures either from the field of pattern recognition or from the field of computer graphics.

Due to the unfortunate situation with the coronavirus (as of April 2020), it is not possible to start the course in the traditional face-to-face manner. We start with an 'inverted classroom' approach, where we pre-record lectures and upload them. Students are required to watch them before the actual lecture period.

The actual lecture period (over Zoom) is dedicated to solving doubts and answering queries that students might have for the lectures watched.

Recommended Literature

Richard Szeliski: Computer Vision: Algorithms and Applications, Springer 2011. Richard Hartley and Andrew Zisserman: Multiple view geometry in Computer Vision. Cambridge university press, 2003.

Additional information

Keywords: computer vision; stereo vision; structure from motion; multi-view reconstruction; convolutional neural networks

Expected participants: 200