Course details
Computer Vision (in English)
POVa Acad. year 2020/2021 Winter semester 5 credits
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.
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Guarantor
Deputy Guarantor
Language of instruction
Completion
Time span
Assessment points
Department
Lecturer
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT)
Najman Pavel, Ing. (DCGM FIT BUT)
Šolony Marek, Ing., PhD. (DCGM FIT BUT)
Španěl Michal, Ing., Ph.D. (DCGM FIT BUT)
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT)
Instructor
Behúň Kamil, Ing. (DCGM FIT BUT)
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT)
Juránek Roman, Ing., Ph.D. (DCGM FIT BUT)
Sochor Jakub, Ing. (DCGM FIT BUT)
Špaňhel Jakub, Ing. (DCGM FIT BUT)
Subject specific learning outcomes and competences
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.
Generic learning outcomes and competences
The students will improve their teamwork skills, mathematics, and exploitation of the "C", C++, and other languages.
Learning objectives
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.
Why is the course taught
The goal of the course eduation is to invoke interest among students about the principles, tasks, and methods of computer vision. The scope of the course cannot reach the broad area of computer vision but brings at least "bits"of it and shows also results and direction of research at FIT.
Study literature
- 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: 978-9386858146
- Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach, Prentical Hall 2011, ISBN: 978-0136085928
Fundamental literature
- 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
Syllabus of lectures
- 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)
- Analysis and Feature Extraction from Images (Čadík 5.11. slajdy)
- Convolutional Neural Networks and Automatic Image Tagging (Hradiš, 12.11. slajdy, video)
- Test, Invariant Image Regions (Beran, 19.11. slajdy)
- Image Registration (Čadík, 26.11., slajdy)
- 3D Computer Vision - Stereo (3.12. Najman, slajdy)
- 3D Computer Vision -SLAM (10.12. Šolony, slajdy)
- Acceleration of Processing in Computer Vision (Zemčík, 17.12., slajdy)
NOTE: The topics and dates are just FYI, not guaranteed, and will be continuously updated.
Syllabus - others, projects and individual work of students
- Homeworks (4-5 runs) at the beginning of semester
- Individually assigned project for the whole duration of the course.
Progress assessment
Homeworks, Mid-term test, individual project.
Schedule
Day | Type | Weeks | Room | Start | End | Lect.grp | Groups | Info |
---|---|---|---|---|---|---|---|---|
Mon | exam | 2021-01-25 | E104 E105 | 15:00 | 16:50 | 1EIT 1MIT 2EIT 2MIT INTE | 3rd term | |
Wed | exam | 2021-01-20 | E104 E112 | 16:00 | 17:50 | 1EIT 1MIT 2EIT 2MIT INTE | 2nd term | |
Thu | exam | 2021-01-14 | M103 M104 M105 N103 | 13:00 | 14:50 | 1EIT 1MIT 2EIT 2MIT INTE | 1st term | |
Thu | lecture | lectures | E105 | 13:00 | 14:50 | 1EIT 1MIT 2EIT 2MIT INTE | NCPS NVIZ xx | |
Thu | lecture | 3., 4., 6. of lectures | E105v | 13:00 | 14:50 | TM |
Course inclusion in study plans
- Programme IT-MSC-2, field MBI, MBS, MMM, MSK, any year of study, Elective
- Programme IT-MSC-2, field MGM, MGMe, MPV, any year of study, Compulsory-Elective group G
- Programme IT-MSC-2, field MIN, any year of study, Compulsory-Elective group I
- Programme IT-MSC-2, field MIS, 2nd year of study, Elective
- Programme MITAI, specialisation NADE, NBIO, NEMB, NGRI, NHPC, NIDE, NISD, NISY, NMAL, NMAT, NNET, NSEC, NSEN, NSPE, NVER, any year of study, Elective
- Programme MITAI, specialisation NCPS, NVIZ, any year of study, Compulsory