Computer Vision (VU)

Course Details


Robert Sablatnig
Sebastian Zambanini
Marco Peer




Vorlesung mit Übung (VU)





    • Up to further notice, Computer Vision (VU) will be held again virtually in WS2021/2022. A timetable for orientation in the self-study of the lecture part is provided here. It is advisable to follow this schedule in order to acquire the necessary theoretical knowledge for the exercise part of this course in time.
    • Lecture recordings are available in the TUWEL course under ‘Lecture Recordings’.
    • Exercise part: TH 9:00 – 11:00 as well as 11:00-13:00 in a 2-week rhythm, see schedule in TUWEL (starts on 07.10.2021).


    • Lecture part: virtual self-study via lecture recordings of WS 2019/2020
    • Exercise part – tutor hours: virtual via Zoom ( 1 registration per group via TUWEL required)

The exercise part will be handled via the associated TUWEL course (This will be visible for students at the beginning of October). In the course of the semester, various tasks are to be solved for the exercise part in groups of 3 using Python. For support, there will be supervised practice times, which will take place 6x in the semester (approximately every 2 weeks). Please register in TUWEL for a group of 3. Attendance is compulsory only for the 1st session on 07.10.2021.

Lecture exam: Registration in TISS


Computers are still limited in their ability to interact with the world and with human users because they cannot “see.” When dealing with computer vision, we must also try to understand the physics of the world, for example, how light reflects off surfaces, how objects move, and how all of this information is projected onto an image through the optics of the camera. It also requires to develop algorithms that reconstruct some of these physical properties from one or more images. This “inverse” problem is actually a big puzzle. Information is lost when the three-dimensional world is projected onto a two-dimensional image, so how do we recover this information from an image? This course shows the basic algorithms that make this goal achievable and develops methods for solving various inverse problems. But vision is more than just reconstructing the 3D world from 2D images, it also deals with the extraction of semantics. The course also demonstrates basic machine learning and probabilistic inference methods to solve this problem.

Even if you do not plan to study computer vision further, the basic tools and techniques used there can be useful in many other areas. For all those who want to learn more about computer vision, this course provides a good basis for further study.


  • A perspective on many areas of current computer vision research
  • An implementation of programming tasks to gain practical experience with working with images and image sequences
  • The application of linear algebra and calculus for something you can use in the real world