183.585 Computer Vision
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020W, VU, 3.0h, 4.5EC


  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to identify, explain and contrast the most important theories, principles, concepts and algorithms of Computer Vision. Their knowledge and understanding correspond to the state of the art literature in the field of computer vision. They are able to apply appropriate formal-mathematical methods for modeling, abstraction, solution finding and evaluation, and gain problem-formulation and problem-solving skills.

Subject of course

How can computers understand the visual world of humans?

This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics include: Structure of Images, Texture, Scenes, and Context, Feature Based Alignment, Stereo Correspondence, Structure from Motion, Image Stitching, Computational Photography, Image Based Rendering, and Baysian Modeling for Object Recognition. This course is designed for students interested in vision, artificial intelligence, or machine learning. The course offers a broad introduction to the field, the current problems and theories, the basic mathematics, and some interesting algorithms and the possibility to apply the contents learned direct in exercises.


Teaching methods

Frontal presentation and written exam, independent solving and scientific discussion of subject-related examples and submissions. The course consists of a lecture part and an exercise part. The contents and concepts described are explained in the context of the lecture unit and practically tested and applied in the practice section.

Mode of examination


Additional information

Due to the current corona situation, the course will be held as distance learning. Please refer to the information and schedule on the course homepage.

ECTS Breakdown: 4.5 ECTS = 112.5h

24h    Lecture
12h    Development of Python code for solving the practical assignments (with tutor support)
25h    Preparation of report of the practical assigments
0.5h   Exercise interview
49h Exam Preparation
2h   Exam



Course dates

Thu13:00 - 15:0001.10.2020 - 26.11.2020 Vorlesung
Fri13:00 - 15:0002.10.2020 - 27.11.2020 Vorlesung
Wed13:00 - 15:0007.10.2020 - 02.12.2020 Vorlesung
Computer Vision - Single appointments
Thu01.10.202013:00 - 15:00 Vorlesung
Fri02.10.202013:00 - 15:00 Vorlesung
Wed07.10.202013:00 - 15:00 Vorlesung
Thu08.10.202013:00 - 15:00 Vorlesung
Fri09.10.202013:00 - 15:00 Vorlesung
Wed14.10.202013:00 - 15:00 Vorlesung
Thu15.10.202013:00 - 15:00 Vorlesung
Fri16.10.202013:00 - 15:00 Vorlesung
Wed21.10.202013:00 - 15:00 Vorlesung
Thu22.10.202013:00 - 15:00 Vorlesung
Fri23.10.202013:00 - 15:00 Vorlesung
Wed28.10.202013:00 - 15:00 Vorlesung
Thu29.10.202013:00 - 15:00 Vorlesung
Fri30.10.202013:00 - 15:00 Vorlesung
Wed04.11.202013:00 - 15:00 Vorlesung
Thu05.11.202013:00 - 15:00 Vorlesung
Fri06.11.202013:00 - 15:00 Vorlesung
Wed11.11.202013:00 - 15:00 Vorlesung
Thu12.11.202013:00 - 15:00 Vorlesung
Fri13.11.202013:00 - 15:00 Vorlesung

Examination modalities

Preparation of practical examples, submission discussion, written exam

Course registration

Begin End Deregistration end
14.09.2020 00:00 13.10.2020 23:59 13.10.2020 23:59

Registration modalities

Student groups are formed by registering for one of the groups listed in TISS. Please register for a group at your preferred time of day: Thursday 9-11 or 11-13. In the first session  the group assignments will be fixed.




The course is based on a new book, Computer Vision: Algorithms and Applications, by Richard Szeliski, which is on the web for free right now.


Previous knowledge

  • Algebra and Discrete Mathematics
  • Algorithms and Data Structures
  • Analysis
  • program design
  • Foundations of Visual Computing

of the currently valid curriculum of computer science bachelor's degree media and visual computing.



if required in English