Imaging Technologies

Study Program: Electrical Engineering, 2nd Bologna Cycle
Semester: winter semester
Credits: 6

Lecturer: Assoc. Prof. Janez Perš, PhD
Assistant: Asst. Marija Ivanovska, MSc

Content (Syllabus outline):

  • Multi-camera systems, multi-camera calibration, structure from motion, active vision.
  • Feature detectors and descriptors, corner detectors, SIFT, HOG, MSER, COV, and others.
  • Multi-resolution, multi-scale approaches.
  • Deformable models, active contour models, active shape models, active appearance models.
  • Image matching and registration, similarity measures, registration models.
  • Object detection and tracking, tracking by detection, Kalman filters, particle filters.
  • Deep learning.
  • Computer vision and machine vision applications.

Objectives and competences:

The aims of this course are to cover selected topics of computer vision and to prepare students for team and independent research and development work.

Intended learning outcomes:

Be able to implement advanced computer vision algorithms. Be able to provide solutions to moderately complex problems.

Learning and teaching methods:

Lectures, laboratory work, home work, project.


  • D. Forsyth, J. Ponce, Computer vision, a modern approach, Prentice Hall, 2003.
  • R. Gonzales, R. Woods, Digital image processing, 2nd Ed., Prentice Hall, 2002.
  • E. Trucco, A. Verri, Introductory techniques for 3-D computer vision, Prentice Hall, 1998.
  • M. Sonka, V. Hlavac, R. Boyle, Image processing, analysis and machine vision, Chapman and Hall Computing series, 1993.
  • A. Bovik (Ed.), Handbook of image and video processing, 2nd ed., Elsevier AP, 2005.