Computer and Robot Vision

Course Description

Robot vision model. Sampling and image formation. Perspective transformations. Modelling and calibrating cameras. Feature-based alignment. Stereo vision. Texture and texture analysis. Invariant feature extraction in robot vision. Region segmentation. Motion-based segmentation. Dynamical scene analysis. Active vision. Knowledge representation in robot vision systems. Methods for multi-object tracking in videoseqences. Detection and crowd behaviour recognition. Methods of the deep learning for computer vision.

Study Programmes

Postgraduate doctoral study programme


Richard Szeliski (2011.), Computer Vision, Algorithms and Applications, Springer
David A. Forsyth, Jean Ponce (2003.), Computer Vision, A Modern Approach, Pearson, Prentice Hall
Rajalingapaa Shanmgamani (2018.), Deep Learning for Computer Vision, Pack

For students


ID 154808
  Winter semester
L1 English Level
L1 e-Learning