Course detail
Computer Vision (in English)
FIT-POVaAcad. year: 2023/2024
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
Entry knowledge
Rules for evaluation and completion of the course
Aims
The students will get acquainted with the principles and methods of computer vision. They will learn in more detail selected methods and algorithms of vision and image acquiring. They will also get acquainted with the possibilities of the scanned data processing. Finally, they will learn how to apply the gathered knowledge practically.
The students will improve their teamwork skills, mathematics, and exploitation of the "C", C++, and other languages.
Study aids
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-MSC-2 Master's
branch MGMe , 0 year of study, winter semester, compulsory-optional
- Programme IT-MSC-2 Master's
branch MPV , 0 year of study, winter semester, compulsory-optional
branch MIN , 0 year of study, winter semester, compulsory-optional
branch MGM , 0 year of study, winter semester, compulsory-optional
branch MBS , 0 year of study, winter semester, elective
branch MIS , 2 year of study, winter semester, elective
branch MBI , 0 year of study, winter semester, elective
branch MSK , 0 year of study, winter semester, elective
branch MMM , 0 year of study, winter semester, elective - Programme MIT-EN Master's 0 year of study, winter semester, elective
- Programme MITAI Master's
specialization NSPE , 0 year of study, winter semester, elective
specialization NBIO , 0 year of study, winter semester, elective
specialization NSEN , 0 year of study, winter semester, elective
specialization NVIZ , 0 year of study, winter semester, compulsory
specialization NGRI , 0 year of study, winter semester, elective
specialization NADE , 0 year of study, winter semester, elective
specialization NISD , 0 year of study, winter semester, elective
specialization NMAT , 0 year of study, winter semester, elective
specialization NSEC , 0 year of study, winter semester, elective
specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
specialization NCPS , 0 year of study, winter semester, compulsory
specialization NHPC , 0 year of study, winter semester, elective
specialization NNET , 0 year of study, winter semester, elective
specialization NMAL , 0 year of study, winter semester, elective
specialization NVER , 0 year of study, winter semester, elective
specialization NIDE , 0 year of study, winter semester, elective
specialization NEMB , 0 year of study, winter semester, elective
specialization NISY , 0 year of study, winter semester, elective
specialization NEMB up to 2021/22 , 0 year of study, winter semester, elective - Programme IT-MGR-1H Master's
specialization MGH , 0 year of study, winter semester, recommended course
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
- Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zemčík 22.9.)
- Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models/Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 29.9.)
- Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 6.10. slajdy1, slajdy2, slajdy3)
- Scanning object detection, boosted classifiers, acceleration/Detekce objektů oknem, boostované klasifikátory, akcelerace (Zemčík 13.10.)
- Object Detection - Trees, Random Forests, Yolo?/Detekce objektů - Stromy, "Random Forests",Yolo? (Juránek, 20.10. slajdy-en)
- Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 27.10. slajdy)
- Hough Transform, RHT, RANSAC, Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování sekvencí (Hradiš, 3.11. slajdy1, slajdy2, slajdy2-en)
- 3D Computer Vision/3D počítačové vidění (10.11. Šolony)
- xxx International Students Day 17.11. xxx
- Test, Stereovision, SLAM/Stereoviděni, SLAM (24.11. Šolony)
- Invariant Image Regions/Invariantní oblasti obrazu (Beran, 1.12. slajdy)
- Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 8.12. slajdy)
- Image Registration/Registrace obrazu (Čadík, 15.12. slajdy)
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