Module I: Imaging Technologies

 Transition from fully manual computer vision methods to the paradigm of feature point detection and image descriptors in conjunction with learnable classifiers. Visual tracking, motion model concept. Transition to the paradigm of learnable image descriptors and convolutional neural networks. 

1. Fundamentals of the human visual system and the difference between human vision and the classical computer vision methods. 
2. Image datasets and their use for the development and evaluation of modern computer vision algorithms. 
3. Feature point detectors and feature point and region descriptors. SIFT, HOG, MSER, COV, and others. Multiresolution approaches, scale space. 
4. Visual object detection and tracking, tracking with detection. Tracking within the framework of Bayesian sequential recursive filtering. Tracking with the Kalman filter. 
5. Convolutional neural networks, learnable methods for computing visual descriptors. Deep neural networks, their application in automation and robotics.

The aims of this course are to cover selected existing and emerging topics in advanced computer vision, and to prepare students for teamwork, as well as independent work in research and development. 

The lectures provide a theoretical background on particular subjects together with practical examples in Matlab and Python. 

Practical work is being performed as the part of laboratory exercises, and is accomplished in the form of multiple assignments, acquainting students with computer vision algorithms. Students work in groups, consisting of two or three students, and the results are in the form of Matlab and Python code. After completing each part, students present their results to the assistant. 

Homework project addresses the particular problem from the machine vision domain, either robot vision or industrial machine vision applications.