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.

2018W, VO, 1.0h, 1.5EC

Properties

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

Aim of course

- Insight look into the implementation of Computer Vision Applications

- Software packages, libraries, and tools used in Computer Vision Applications

Subject of course

  • 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, ¿

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 principled 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, modern cameras can detect faces in real-time, and we will see how this works. Other topics include Kinect¿s depth and pose estimation as well as deep learning, one of the current ¿hot topics¿ in computer vision.

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Wed10:00 - 12:0010.10.2018 - 30.01.2019Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed10:00 - 12:0028.11.2018Seminarraum FAV 01 C (Seminarraum 188/2) Statusberichte
Wed10:00 - 12:0019.12.2018Seminarraum FAV 01 C (Seminarraum 188/2) Vorlesung
Computer Vision Systems Programming - Single appointments
DayDateTimeLocationDescription
Wed10.10.201810:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed17.10.201810:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed24.10.201810:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed07.11.201810:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed28.11.201810:00 - 12:00Seminarraum FAV 01 C (Seminarraum 188/2) Statusberichte
Wed19.12.201810:00 - 12:00Seminarraum FAV 01 C (Seminarraum 188/2) Vorlesung
Wed16.01.201910:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed23.01.201910:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung
Wed30.01.201910:00 - 12:00Seminarraum FAV 01 A (Seminarraum 183/2) Vorlesung

Examination modalities

Oral Exam

Exams

DayTimeDateRoomMode of examinationApplication timeApplication modeExam
Wed - 22.01.2020oral19.01.0020 00:00 - 22.01.2020 00:00TISSVO Prüfung

Course registration

Begin End Deregistration end
30.08.2018 00:00 10.10.2018 00:00 28.11.2018 00:00

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

German