Introduction to Computer Vision (English)

Termin(e)
Lecture /w Excercise

Tuesday, 3 - 5pm; Monday 1-3pm; Friday 3-5pm G29-335; G29-K059

Prof. Dr.-Ing. Klaus Tönnies

Enrollment:

This course is limited to 36 students only. If you are pre-registered within the LSF you will receive an e-mail with registration details for the OvGU’s moodle system shortly. The admission exam will take place online within moodle during the 15th of April (all day: GMT+2). Please make sure to participate during this time. There will be NO exceptions if you cannot manage to take part in the online exam.

Computer vision is derived from the term human vision (i.e. visual perception of the human being). Computer vision methods describe processes of visual perception algorithmically on different levels or replace them with equivalent processes, so that recorded camera images can be automatically interpreted or analyzed. Computer Vision may be differentiated into Early Vision and High Level Vision. Early Vision methods derive generic information such as depth or motion from images whereas High Level Vision focuses on the analysis of objects depicted in the images. Examples are object classification or object detection. The aim this lecture series is to present basic algorithms from Early Vision and High Level Vision, which are part of many methods for image-based analysis in industry and research.

Lecture contents:

  1. Introduction
  2. Early Vision – Noise Suppression, Scale Space and Segmentation
  3. Early Vision – Features from Images
  4. Early Vision – The reconstruction of Depth (introduction)
  5. Stereo Vision I – Epipolar Geometry
  6. Stereo Vision II – Correspondence Analysis
  7. Motion – Optical Flow and Structure from Motion
  8. High Level Vision – The detection of Objects
  9. Template Matching
  10. Variable Templates
  11. Part-Based Models
  12. Object Tracking
  13. Object Classification (Introduction)

Credits: 5

Language: English