Course detail
Computer Vision (in English)
FIT-POVaAcad. year: 2022/2023
Principles and methods of computer vision, methods and principles of image acquiring, preprocessing methods (statistical processing), filtering, pattern recognition, integral transformations - Fourier transform, image morphology, classification problems, automatic classification, D methods of computer vision, open problems of computer vision.
Language of instruction
Number of ECTS credits
Mode of study
Offered to foreign students
Learning outcomes of the course unit
The students will improve their teamwork skills, mathematics, and exploitation of the "C", C++, and other languages.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
IEEE Multimedia, IEEE, USA - série časopisů - různé články (EN)
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146 (EN)
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146 (EN)
(EN)
Elearning
Classification of course in study plans
- Programme IT-MGR-1H Master's
branch MGH , 1 year of study, winter semester, recommended course
- Programme IT-MSC-2 Master's
branch MGMe , 0 year of study, winter semester, compulsory-optional
- Programme IT-MSC-2 Master's
branch MBI , 0 year of study, winter semester, elective
branch MBS , 0 year of study, winter semester, elective
branch MIS , 2 year of study, winter semester, elective
branch MMM , 0 year of study, winter semester, elective
branch MSK , 0 year of study, winter semester, elective - Programme MIT-EN Master's 0 year of study, winter semester, elective
- Programme MITAI Master's
specialization NADE , 0 year of study, winter semester, elective
specialization NBIO , 0 year of study, winter semester, elective
specialization NCPS , 0 year of study, winter semester, compulsory
specialization NEMB , 0 year of study, winter semester, elective
specialization NGRI , 0 year of study, winter semester, elective
specialization NHPC , 0 year of study, winter semester, elective
specialization NIDE , 0 year of study, winter semester, elective
specialization NISD , 0 year of study, winter semester, elective
specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
specialization NMAL , 0 year of study, winter semester, elective
specialization NMAT , 0 year of study, winter semester, elective
specialization NNET , 0 year of study, winter semester, elective
specialization NSEC , 0 year of study, winter semester, elective
specialization NSEN , 0 year of study, winter semester, elective
specialization NSPE , 0 year of study, winter semester, elective
specialization NVER , 0 year of study, winter semester, elective
specialization NVIZ , 0 year of study, winter semester, compulsory
specialization NISY , 0 year of study, winter semester, elective - Programme IT-MSC-2 Master's
branch MGM , 0 year of study, winter semester, compulsory-optional
branch MIN , 0 year of study, winter semester, compulsory-optional
branch MPV , 0 year of study, winter semester, compulsory-optional - Programme MITAI Master's
specialization NEMB up to 2021/22 , 0 year of study, winter semester, elective
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zemčík 23.9.)
- Basic principles of machine learning with teacher - AdaBoost/Základní principy klasifikace s učitelem - AdaBoost (Zemčík 30.9.)
- Hough Transform, RHT, RANSAC, Time Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování časových sekvencí (Hradiš, 7.10.)
- Object Detection - Trees, Random Forests/Detekce objektů - Stromy, "Random Forests" (Juránek, 14.10.)
- Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models / Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 21.10.)
- Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 4.11.)
- Image Registration/Registrace obrazu (Čadík, 11.11.)
- Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 18.11.)
- Test, Invariant Image Regions/Invariantní oblasti obrazu (Beran, 25.11.)
- Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 2.12.)
- 3D Computer Vision - Stereo/3D počítačové vidění - stereo, SLAM (9.12. Šolony)
- Acceleration of Processing in Computer Vision/Akcelerace výpočtů v počítačovém vidění (Zemčík, 16.12.)
NOTE: The topics and dates are just FYI, not guaranteed, and will be continuously updated.
POZOR!!! Témata přednášek i data jsou orientační a budou v průběhu semestru aktualizována.
Project
Teacher / Lecturer
Syllabus
- Homeworks (4-5 runs) at the beginning of semester
- Individually assigned project for the whole duration of the course.
Elearning