|Lecture /w Excercise|
Tuesday, 3 - 5pm; Monday 1-3pm; Friday 3-5pm G29-335; G29-K059
|Prof. Dr.-Ing. Klaus Tönnies|
This course is limited to 36 students only. First, please duly enroll at the dedicated lecture page within the LSF (registration is open from the 25.02.2020-31.03.2020).
Since we expect many more applicants than places are available, students have to successfully pass an admission exam in order to be allowed to enroll in this course.
The admission exam will take place on the 2nd of April 10am in G29-307
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.
- Early Vision – Noise Suppression, Scale Space and Segmentation
- Early Vision – Features from Images
- Early Vision – The reconstruction of Depth (introduction)
- Stereo Vision I – Epipolar Geometry
- Stereo Vision II – Correspondence Analysis
- Motion – Optical Flow and Structure from Motion
- High Level Vision – The detection of Objects
- Template Matching
- Variable Templates
- Part-Based Models
- Object Tracking
- Object Classification (Introduction)
To be allowed to take part in the examination, students have to successfully participate in an accompanying project. Details will be discussed within the first lecture.