Lecturer: Oliver Bimber

Objectives

While Computer Graphics focusses on image synthesis, Computer Vision is all about image analysis and image understanding. It finds many applications in domains such as, 3D reconstruction, robotics, medical engineering, media technology, automatization, biometry, human-computer-interaction, contact free measurement, remote sensing, quality control, etc. This lecture will give first insights into the basics of Computer Vision and links to corresponding machine learning approaches. At the end of the semester, participants of this class will be able to apply and implement computer vision methods independently. A basic understanding of programming concepts is required. Detailed knowledge in a programming language, however, is not necessary. The associated lab will provide a sufficient introduction into python, python modules, and hands-on computer vision techniques to prepare students for their team projects.

Subject

Spatial and frequency domain processing, gradient domain processing, segmentation and object recognition, basics of cameras, geometric camera calibration, the geometry of multiple views, stereoscopic depth estimation, range scanning and data processing, structure from motion, computational photography, machine learning approaches used for computer vision.

Selected Readings

1. Computer Vision – A Modern Approach, Forsyth and Ponce, Addison Wesley
2. Multiple View Geometry in Computer Vision, Hartley and Zisserman, Cambridge Press
3. Computer Vision: Algorithms and Applications, Richard Szeliski, Springer
4. Image Processing: The Fundamentals, Maria Petrou and Costas Petrou, Wiley
5. Learning OpenCV: Computer Vision with the OpenCV Library, Gary Bradski, Adrian Kaehler, Mike Loukides, Robert Romano, O'Reilly
6. Handbook of Mathematical Models in Computer Vision, Nikos Paragios and Yunmei Chen, Springer
7. Machine Vision. Theory, Algorithms, Practicalities: Theory, Algorithms, Practicalities: Theory, Algorithms, Practicalities, E. R. Davies, Academic Press
8. Computational Vision: Information Processing in Perception and Visual Behavior, Hanspeter A. Mallot, MIT Press
9. Three-Dimensional Computer Vision – A Geometric Approach, Olivier Faugeras, MIT Press

Criteria for Evaluation

eExam (Moodle Test)

Format

The lecture format is hybrid: presence in lecture halls Linz, Vienna, Bregenz + Zoom.