Computer Vision

Students will understand all the layers of the computer based systems including hardware (semiconductor components, logic networks, processors, peripheral devices), software (control and data structures, object orientation, programming languages, compilers, operating systems, databases), as well as their common applications (information systems, computer networks, artificial intelligence, computer graphics and multimedia). They will understand foundations of computer science (discrete mathematics, formal languages and their models, spectral analysis of signals, modelling and simulation). Graduates will be able to analyse, design, implement, test, and maintain common computer applications. They will be able to work efficiently in teams.

State Exam in Information Technology and Artificial Intelligence, specialization Computer Vision consists of the following parts:

• presentation and defense of master's thesis,
• oral exam, which combines the basic themes contained in the courses profiling the basis of Information Technology and Artificial Intelligence (Theoretical Computer Science, Statistics and Probability, Computer Systems Architectures, Artificial Intelligence and Machine Learning, Data Storage and Preparation, Functional and Logic Programming, Parallel and distributed algorithms, Modern trends in informatics),
• oral exam, which combines the basic themes contained in the courses profiling the basis of Computer Vision (Computer Graphics, Computational Geometry, Computer Vision, Image Processing, Graphical and Sound Interfaces and Standards, Convolutionary Neural Networks).

All parts of the state examination are held on the same date before the State Examination Board. The state exam can be taken by a student who has obtained the required number of credits in the prescribed composition necessary for the successful completion of the master's degree and has submitted the master's thesis in due time. The organization and course of the state examination are given by the corresponding internal standard of the faculty and by the relevant instructions of the program guarantor for state examinations.

• Reconstruction of 3D information about automobiles observed by a surveillance camera
• Optical localization of very distant targets in a multi-camera system
• Counting pedestrians in video
• Deep neural networks for face recognition in video
• Recognition of historical texts by using deep neural networks
• Deblurring of X-ray images with geometric blur
• Estimation of personality characteristics from video

Master's theses are stored at the FIT library, Božetěchova 2, Brno. The list of the master's theses, including the details is available at:
https://www.fit.vut.cz/study/theses/