Machine vision

Course description

Modelling of visual systems: physical, mathematical, biological and computational basics. Selected mathematical tools and algorithms for analysis of visual information: selected topics from linear algebra, stochastic systems and information theory.

Selected algorithms for detection and tracking of objects, events, for motion analysis and activity, based on visual information. Multi-sensor visual systems. Biologically motivated architectures for visual sensing. Visual sensor networks and embedded visual systems. Machine vision in industry, visual inspection and measurement.

Machine vision in advanced visual surveillance systems, biometric systems and robots. Use of machine vision in sport, analysis of individual and team activities. Machine vision in advanced user interfaces.

Course is carried out on study programme

Objectives and competences

Getting familiar with engineering, mathematical, physical, algorithmical and biological foundations of visual perception. Preparation for scientific research and development in the field of artificial visual perception systems.

Learning and teaching methods

The course will be comprised of lectures and  project assignments.

Lectures will be given by the lecturer and the co-lecturer.  

Project assigment will be divided into self-contained parts, providing the framework for individual study of selected methods and algorithms. Each of the assignment parts will require written report and  presentation/defense in front of other students.

Important part of the study are discussions in the class. Each candidate also presents a theoretical topic related to the project assignment.

Intended learning outcomes

After completing the course, students will be able to:

independently and critically evaluate state of the art in the field of of artificial visual perception systems.

Perfom doctoral grade research in this field by developing and analyzing novel algorithms and methods.

 Understand the importance of objective, quantitative evaluation of developed methods and have the skills to perform such an evaluation.

Reference nosilca

  1. MUHOVIČ, Jon Natanael, MANDELJC, Rok, BOVCON, Borja, KRISTAN, Matej, PERŠ, Janez. Obstacle tracking for unmanned surface vessels using 3-D point cloud. IEEE journal of oceanic engineering. [Print ed.]. 2019, vol. , str. 1-13, ilustr. ISSN 0364-9059., DOI: 10.1109/JOE.2019.2909507. [COBISS.SI-ID 12642388]
  2. KOPOREC, Gregor, VUČKOVIĆ, Goran, MILIĆ, Radoje, PERŠ, Janez. Quantitative contact-less estimation of energy expenditure from video and 3D imagery. Sensors. Aug. 2018, iss. 8, 4235, str. 1-32, ilustr. ISSN 1424-8220., DOI: 10.3390/s18082435. [COBISS.SI-ID 12120148]
  3. KRISTAN, Matej, SULIĆ KENK, Vildana, KOVAČIČ, Stanislav, PERŠ, Janez. Fast image-based obstacle detection from unmanned surface vehicles. IEEE transactions on cybernetics, ISSN 2168-2267, Mar. 2016, vol. 46, no. 3, pp. 641-654.
  4. 2. Mandeljc R, Kovačič S, Kristan M, Perš J (2013) Tracking by identification using computer vision and radio. Sensors, 13(1):241-273
  5. Sulić V, Perš J, Kristan M, Kovačič S (2011) Efficient feature distribution for object matching in visual-sensor networks. IEEE Trans. Circuits Syst Video Technol 21(7): 903-916
  6.  Kristan M, Kovačič S, Leonardis A, Perš J (2010) A two-stage dynamic model for visual tracking. IEEE Trans Syst Man Cybern B 40(6):1505-1520

Study materials

1. David A. Forsyth, Jean Ponce. Computer Vision: A Modern Approach (2nd Edition), Prentice Hall, 2011

2. Milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing, Analysis, and Machine Vision (4th Edition), Cengage Learning, 2014

3. Richard Szeliski.  Computer Vision: Algorithms and Applications, Springer, 2011, (

4. Pomembnejši znanstveni članki iz tematike (Classic scientific papers on the topic)

Bodi na tekočem

Univerza v Ljubljani, Fakulteta za elektrotehniko, Tržaška cesta 25, 1000 Ljubljana

E: T:  01 4768 411