English

Every spring semester

The aim of the course is to give necessary knowledge for further research within computer vision and in order to use computer vision methods within other research areas, e.g. robotics, vision systems, non-invasive measurements and augmented reality.

Projective geometry. Geometric transformations. Modelling cameras. Stereo vision. Photogrammetry. Recognition. 3D-modelling. Geometry of surfaces and their silhouettes. Visualisation.

be able to clearly explain and use basic concepts in computer vision, in particular regarding projective geometry, camera modelling, stereo vision, recognition and structure and motion problems.

be able to describe and give an informal explanation of the mathematical theory behind some central algorithms in computer vision (the least squares method and Newton based optimization).

in an engineering manner be able to use computer packages to independently solve problems in computer vision.

be able to show good ability to independently identify problems which can be solved with methods from computer vision, and be able to choose an appropriate method.

be able to independently apply basic methods in computer vision to problems which are relevant in industrial applications or research.

with proper terminology, in a well-structured way and with clear logic, be able to explain the solution to a problem in computer vision.

Lectures

Laboratory exercises

Exercises

Written exam

Oral exam

Written assignments

Failed, pass

Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, 2010. ISBN 9781848829343.

It is possible to pass the course using handouts.

FMA271F

 -01-31

FN1/Anders Gustafsson