Machine vision

This course delves into the modelling of visual systems, covering the physical, mathematical, biological, and computational fundamentals. It incorporates selected mathematical tools and algorithms for the analysis of visual information, highlighting topics from linear algebra, stochastic systems, and information theory. 

The course further explores algorithms for the detection and tracking of objects and events, motion analysis, and the assessment of activities based on visual information. Special emphasis is placed on multi-sensor visual systems and biologically motivated architectures for visual sensing, including visual sensor networks and embedded visual systems. 

The application of machine vision in industrial contexts, such as visual inspection and measurement, is examined. Additionally, the course addresses the use of machine vision in advanced visual surveillance systems, biometric systems, and robotics, showcasing its impact across various fields. 

The role of machine vision in sports for analyzing individual and team activities is discussed, alongside its integration into advanced user interfaces. This comprehensive approach offers insights into the evolving landscape of machine vision and its applications, fostering a deeper understanding of how visual systems can enhance interaction and interpretation of the visual world. 

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.

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.