Computer Vision
Datorseende

FMA271F, 7.5 credits

Valid from: Autumn 2013
Decided by: FN1/Anders Gustafsson
Date of establishment: 2014-01-31

General Information

Division: Mathematics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMA270
Teaching language: English

Aim

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.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• 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).

Competences and Skills

For a passing grade the doctoral student must

• 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.

Course Contents

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

Course Literature

Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, 2010. ISBN 9781848829343.
It is possible to pass the course using handouts.

Instruction Details

Types of instruction: Lectures, laboratory exercises, exercises

Examination Details

Examination formats: Written exam, oral exam, written assignments
Grading scale: Failed, pass
Examiner:

Admission Details

Course Occasion Information

Contact and Other Information

Course coordinators: