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.

2022W, VO, 1.0h, 1.5EC
TUWEL

Course evaluation

Properties

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

Learning outcomes

After successful completion of the course, students are able to plan and assess software development projects in the field of computer vision. Key findings include data collection and assessment of data quality, implementation and evaluation of results.

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, ¿

Teaching methods

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

Mode of examination

Oral

Additional information

Computer vision from an applied point of view. We will review popular programming languages as well as open and closed source software (e.g. Matlab, NumPy, OpenCV) and talk about their pros and cons. We will also talk about how to approach computer vision problems in a principle way, and how related topics such as image processing, probability theory, numerical optimization, and machine learning fit into the picture. For the most part we will talk about selected successful computer vision applications. For example, video or RGBD cameras detect faces or certain behavior 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.

 

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Wed09:00 - 11:0005.10.2022 - 25.01.2023Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Computer Vision Systems Programming - Single appointments
DayDateTimeLocationDescription
Wed05.10.202209:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) 1st meeting
Wed12.10.202209:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) 1st lecture
Wed09.11.202209:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) 2nd lecture
Wed23.11.202209:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) 3.Vorlesung
Wed18.01.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Excursion
Wed25.01.202309:00 - 11:00Seminarraum FAV 01 A (Seminarraum 183/2) Exam

Examination modalities

Mündliche Prüfung

Course registration

Begin End Deregistration end
01.09.2022 00:00 19.10.2022 00:00 28.10.2022 01:00

Curricula

Literature

No lecture notes are available.

Previous knowledge

Basic image processing and computer vision knowledge is expected (e.g. what is linear filtering? what is a camera matrix?).

Accompanying courses

Miscellaneous

Language

if required in English