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

2023W, VO, 1.0h, 1.5EC


  • Semester hours: 1.0
  • Credits: 1.5
  • Type: VO Lecture
  • Format: Presence

Learning outcomes

After successful completion of the course, students are able to estimate and also carry out development projects in the field of computer vision. Important insights concern all individual steps of a computer vision project: this starts with planning and effort estimation, includes data acquisition and data quality assessment, implementation and evaluation of the results, but also questions of interdisciplinary collaboration, as well as legal and ethical considerations.

Subject of course

Overview on CV Languages, Libraries and Applications

  • Computer Vision Programming Languages: C++, Python, Matlab
  • Computer Vision Software: Matlab, OpenCV, NumPy, Scikit-Learn, 
  • Computer Vision Applications: Face Recognition, Human Pose Estimation, Deep Learning, 

Examples from everyday research, results from CV research projects

Excursion or visit to a company in the field of computer vision.

Teaching methods

  • Literature search and paper selection
  • Data quality assesment
  • Evaluation and testing

Mode of examination


Additional information

Computer vision from an applied point of view. We will review CV projects and solutions, have a closer look on the selected programming languages, models and/or datasets and talk about their pros and cons. We will also talk about how to approach computer vision problems in a principle and in practical way. You will see that computer vision solutions rely on cross disciplinary expertise and you will learn how interdisciplinary cooperations ranging from medical doctors, police and justice, archaeologists and numismatists, care givers and elderly people on one side and computer scientists on the other side work. However we focus on technology, for example, video, RGBD or thermal cameras detect faces or certain behaviour in real-time, and we will see how this works. Other topics include depth and pose estimation as well as deep learning, one of the current "hot topics" in computer vision.



Course dates

Wed09:00 - 11:0004.10.2023 - 24.01.2024Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Computer Vision Systems Programming - Single appointments
Wed04.10.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Preliminary meeting
Wed18.10.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) CV-Systems, Applications and Projects
Wed08.11.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Presentation of Lab topics
Wed22.11.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Software Packages
Wed13.12.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) From Research to Products
Wed20.12.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Status Reports LU
Wed10.01.202409:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Reports LU
Wed17.01.202409:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Excursion
Wed24.01.202409:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Final Presentations

Examination modalities

Mündliche Prüfung


DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Wed09:00 - 11:0022.01.2025 onlineoral28.09.2024 00:00 - 21.01.2025 08:00TISSVO Prüfung

Course registration

Begin End Deregistration end
26.07.2023 00:00 18.10.2023 00:00 27.10.2023 01:00


Study CodeObligationSemesterPrecon.Info
066 932 Visual Computing Mandatory elective
066 936 Medical Informatics Mandatory elective


No lecture notes are available.

Previous knowledge

Basic interest in practical solutions in the field of computer vision is recommended for the lecture.

Accompanying courses



if required in English