Machine Vision

Higher education teachers: Perš Janez

Subject description

Prerequisits:

• Enrolment into the doctoral study programme

Content (Syllabus outline):

• 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.

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.

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. They will have the skills to perform doctoral grade research in this field by developing and analyzing novel algorithms and methods. They will understand the importance of objective, quantitative evaluation of developed methods and have the skills to perform such an evaluation.

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.

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, (http://szeliski.org/Book)