Higher education teachers: Likar Boštjan

Higher education teachers: , Špiclin Žiga
Credits: 6
Semester: summer
Subject code: 64235

Subject description


Enrolment into the 1st year of the Master’s study programme of Electrical Engineering

Content (Syllabus outline):

  • Visual perception: light, human vision, cameras, illumination, image quality, sampling and quantification, image formats and standards.
  • Digital image processing and restoration: smoothing and sharpening, statistical and morphological filtering, image resampling, geometrical transformations.
  • Robust extraction of 2D objects: points, lines, corners, polygons, circles, ellipses, templates and nonparametric models.
  • Calibration of imaging systems: geometry, sensitivity, spatial homogeneity, temporal stability, self-calibration.
  • 3D object reconstruction: stereo vision, structured light, shape from shading, fitting 3D models to 2D images
  • Visual navigation: tracking, filtration and motion analysis, visual feedback-based robot control
  • Applications of robot vision: visual quality control, product sorting, object and obstacle detection, environment modelling, trajectory planning

Objectives and competences:

The objective of this course is to: introduce the main building blocks of a robot vision system and the fundamental associated problems; to introduce the main concepts and techniques used to solve those problems; to enable students to implement solutions for rather simple problems; to enable students to understand the basic methodology that is discussed in the robot vision literature.

Intended learning outcomes:

The students will obtain an overview of robot vision technologies and systems, basic building blocks of the systems, and basic image processing and analysis methods for the detection, recognition and measurement of objects in a scene, and for visual guidance of robots.

Learning and teaching methods:

Lectures, lab works and individual assignements.

Study materials


  1. Wilhelm Burger in Mark J. Burge. Principles of Digital Image Processing: Fundamental Techniques, Springer, 2009.
  2. Wilhelm Burger in Mark J. Burge. Principles of Digital Image Processing: Core Algorithms, Springer; 1st Edition. 2nd Printing, 2011.
  3. Berthold K. P. Horn. Robot Vision, MIT Press, 1986.
  4. Alexander Hornberg (Editor), Handbook of Machine Vision, Wiley-VCH, 2006.
  5. E. R. Davies. Machine Vision: Theory, Algorithms, Practicalities, , Morgan Kaufmann, 3rd edition, 2004.

Study in which the course is carried out

  • 1 year - 2nd cycle - Electrical Engineering - Robotics