Machine Perception (FRI)

Higher education teachers: Kristan Matej
Credits: 6
Semester: winter
Subject code: 63267

Subject description


As specified by internal acts of the University of Ljubljana and Faculty of Computer and Information Science.

Content (Syllabus outline):


  • Overview of the field of Machine perception and scientific challenges
  • Image processing

Image formation

  • Binarization, morfology, segmentation
  • Colour spaces and colour perception
  • Linear and nonlinear filters

Image derivatives and edge perception

  • Derivative-based edge perception
  • Edge-based object perception
  • Parametric shape perception

Model fitting

  • Normal equations
  • Homogenous systems
  • Robust approaches

Local features

  • Corner perception
  • Local descriptors in scale space and affine adaptation

Stereoscopy and depth perception

  • Calibrated and uncalibrated systems and reconstruction

Object recognition

  • Subspace methods (PCA, LDA)
  • Local-features-based recognition

Object detection

  • Visual features and detection approaches

Motion perception

  • Local motion perception and object tracking


Exercises will take a form of project-oriented exercises in properly equipped student laboratories. Students will implement various algorithms and test them on different datasets using a variety of sensor systems. Exercises will support an in-depth understanding of the theory. They will also encourage independent thinking and creativity.

Objectives and competences:

In the framework of this course, the students will acquire concrete knowledge and skills in the area of machine perception. The students will develop competences in low-level image processing, 3D geometry of stereo systems, object detection, object recognition, and motion extraction in video sequences. The students will also practice mathematical basics crucial for solving demanding engineering problems, which are essential for analysis of complex signals such as images and video.

In addition, the students will obtain the following competences:

  • The ability to understand and solve professional challenges in computer and information science.
  • The ability of professional communication in the native language as well as a foreign language.
  • The ability to independently perform both less demanding and complex engineering and organisational tasks in certain narrow areas and independently solve specific well-defined tasks in computer and information science.

Intended learning outcomes:

Knowledge and understanding: Understanding of computer technology and computational methodology for use and development of components for machine vision systems.

Application: Use of computer technology and computational methodology for specific applications of autonomous intelligent cognitive systems.

Reflection: Understanding how the theory can be tuned for different application scenarios in the area of intelligent perceptual/cognitive systems.

Transferable skills: Solving other conceptually similar problems (e.g., other modalities) based on the models of machine and artificial cognitive perception.

Learning and teaching methods:

Lectures, laboratory exercises in computer classroom with active participation. Individual work on exercises. Theory from the lectures made concrete with hands-on laboratory exercises. Special emphasis will be put on continuous assessment at exercises.

Study materials


  • D. Forsyth and J. Ponce, Computer Vision: A modern approach, Prentice Hall 2011.
  • R. Szeliski,Computer Vision: Algorithms and Applications, Springer, 2011


  • H. R. Schiffman: Sensation and Perception, An Integrated Approach, John Wilez & Sons 2001.

Izbrani članki iz revij IEEE PAMI, CVIU, IJCV, Pattern Recognition (dostopno na spletu)

Study in which the course is carried out

  • 3 year - 1st cycle - Multimedia