Course detail
Computer Vision (in English)
FIT-POVaAcad. year: 2020/2021
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.
Supervisor
Nabízen zahradničním studentům
Všech fakult
Learning outcomes of the course unit
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.
Prerequisites
Not applicable.
Co-requisites
Not applicable.
Recommended optional programme components
Not applicable.
Recommended or required reading
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146
Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
IEEE Multimedia, IEEE, USA - série časopisů - různé články
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Planned learning activities and teaching methods
Not applicable.
Assesment methods and criteria linked to learning outcomes
Homeworks, Mid-term test, individual project.
Language of instruction
English
Work placements
Not applicable.
Aims
To get acquainted with the principles and methods of computer vision. To learn in more detail selected methods and algorithms of vision and image acquiring. To get acquainted with the possibilities of the scanned data processing. To learn how to apply the gathered knowledge practically.
Classification of course in study plans
- Programme IT-MGR-2 Master's
branch MBI , any year of study, winter semester, 5 credits, elective
branch MPV , any year of study, winter semester, 5 credits, compulsory-optional
branch MGM , any year of study, winter semester, 5 credits, compulsory-optional - Programme IT-MGR-2 Master's
branch MGMe , any year of study, winter semester, 5 credits, compulsory-optional
- Programme IT-MGR-2 Master's
branch MSK , any year of study, winter semester, 5 credits, elective
branch MBS , any year of study, winter semester, 5 credits, elective
branch MIN , any year of study, winter semester, 5 credits, compulsory-optional
branch MMM , any year of study, winter semester, 5 credits, elective - Programme MITAI Master's
specialization NADE , any year of study, winter semester, 5 credits, elective
specialization NBIO , any year of study, winter semester, 5 credits, elective
specialization NGRI , any year of study, winter semester, 5 credits, elective
specialization NNET , any year of study, winter semester, 5 credits, elective
specialization NVIZ , any year of study, winter semester, 5 credits, compulsory
specialization NCPS , any year of study, winter semester, 5 credits, compulsory
specialization NSEC , any year of study, winter semester, 5 credits, elective
specialization NEMB , any year of study, winter semester, 5 credits, elective
specialization NHPC , any year of study, winter semester, 5 credits, elective
specialization NISD , any year of study, winter semester, 5 credits, elective
specialization NIDE , any year of study, winter semester, 5 credits, elective
specialization NISY , any year of study, winter semester, 5 credits, elective
specialization NMAL , any year of study, winter semester, 5 credits, elective
specialization NMAT , any year of study, winter semester, 5 credits, elective
specialization NSEN , any year of study, winter semester, 5 credits, elective
specialization NVER , any year of study, winter semester, 5 credits, elective
specialization NSPE , any year of study, winter semester, 5 credits, elective - Programme IT-MGR-1H Master's
branch MGH , any year of study, winter semester, 5 credits, recommended
- Programme IT-MGR-2 Master's
branch MIS , 2. year of study, winter semester, 5 credits, elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
- Introduction, motivation and applications (Zemčík 24.9. slides, slajdy, highlights)
- Basic principles of machine learning with teacher - AdaBoost (Zemčík 1.10. slajdy-cz, slides-en)
- Object Detection - Trees, Random Forests (Juránek, 8.10. slajdy-en)
- Hough Transform, RHT, RANSAC, Time Sequence Processing (Hradiš, 15.10. slajdy1, slajdy2, slajdy2-en)
- Clustering, statistical methods (Španěl 22.10. slajdy)
- Segmentation, colour analysis, histogram analysis (Španěl 29.10. slajdy1, slajdy2, slajdy3)
- Analysis and Feature Extraction from Images (Čadík 5.11. slajdy)
- Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 12.11. slajdy)
- Test, Invariant Image Regions (Beran, 19.11. slajdy)
- Image Registration (Čadík, 26.11., slajdy)
- 3D Computer Vision - Stereo (3.12. Najman + guest Richter FEKT slajdy)
- 3D Computer Vision -SLAM (10.12. Šolony)
- Acceleration of Processing in Computer Vision (Zemčík, 17.12.)
NOTE: The topics and dates are just FYI, not guaranteed, and will be continuously updated.
Project
26 hours, compulsory
Teacher / Lecturer
Syllabus
- Homeworks (4-5 runs) at the beginning of semester
- Individually assigned project for the whole duration of the course.