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263-5902-00L 8 Credits BSC , MSC , WBZ D-HEST , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ERDW , D-ITET , D-GESS

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Computer Vision

Lecturers & Examiners: Prof. Dr. Marc Pollefeys, Prof. Dr. Siyu Tang

VVZ CR 2.38

Last Updated: 2026-02-05 16:30:25

Abstract

The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises.

Objective

The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature.

Content

Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition

Resources

Learning Materials (Links)

Main link
Information

General Information

Language: English
Levels: BSC , MSC , WBZ
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: None

The grade will be determined by a) a written final exam and b) homework exercises performed during the semester (compulsory continuous performance assessment). Specifically, the final grade will be 3/4 final exam and 1/4 exercises.

Course Components

Type	Title	Time & Place	Hours
lecture	Computer Vision	Wed 14:15-16:00 (NO C 60) Thu 12:15-13:00 (HG G 5)	3 h weekly
exercise	Computer Vision	Thu 13:15-14:00 (CAB G 61) Fri 13:15-14:00 (CAB G 51)	1 h weekly
independent project	Computer Vision	No time listed	3 h weekly

Offered In

Computational Science and Engineering Bachelor
- Fields of Specialization
  - Robotics (continued) (Only one of the two course units 263-5902-00L Computer Vision resp. 227-0447-00L Image Analysis and Computer Vision may be recognised for credits for the overall (CSE Bachelor and Master) study programmes. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits in the field of specialisation `Robotics' for the overall (CSE Bachelor and Master) study programmes. However, the other course unit may be recognised for a different category. For the category assignment take contact with the Study Administration ( ).)
Mechanical Engineering Master
- Core Courses
Computational Science and Engineering Master
- Fields of Specialization
  - Robotics (continued) (Only one of the two course units 263-5902-00L Computer Vision resp. 227-0447-00L Image Analysis and Computer Vision may be recognised for credits for the overall (CSE Bachelor and Master) study programmes. Only one of the two course units 263-5210-00L Probabilistic Artificial Intelligence resp. 252-0535-00L Advanced Machine Learning may be recognised for credits in the field of specialisation `Robotics' for the overall (CSE Bachelor and Master) study programmes. However, the other course unit may be recognised for a different category. For the category assignment take contact with the Study Administration ( ).)
Biomedical Engineering Master
- Track Courses
  - Bioelectronics
    - Recommended Elective Courses (These courses are particularly recommended for the Bioelectronics track. Please consult your track advisor if you wish to select other subjects.)
Computer Science Master
- Majors
  - Major in Visual and Interactive Computing
    - Core Courses
  - Major in Machine Intelligence
    - Elective Courses
- Minors
  - Minor in Computer Vision
CAS in Computer Science
- Focus Courses and Electives
Robotics, Systems and Control Master
- Core Courses
Science, Technology, and Policy Master
- Minor in Natural Sciences and Engineering
  - Data and Computer Science
Data Science Master
- Master Studies (Programme Regulations 2023)
  - Electives
    - Subject-Specific Electives
- Master Studies (Programme Regulations 2017)
  - Core Courses
    - Core Electives
DAS in Data Science
- Specialisation Track
  - Image Analysis & Computer Vision
Cyber Security Master
- Minor
  - Machine Intelligence
    - Elective Courses
  - Visual and Interactive Computing
    - Core Courses
Space Systems Master
- Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
  - Robotics
    - Deep Track Robotics (These courses can be credited either as a specialization subject or as an elective subject.)