Computer Vision (in English)

• 26 hrs lectures
• 26 hrs projects

• 51 pts final exam (26 pts written part, 25 pts test part)
• 9 pts mid-term test (3 pts written part, 6 pts test part)
• 40 pts projects

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.
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.

• Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
• Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN: 978-9386858146
• IEEE Multimedia, IEEE, USA - série časopisů - různé články
• Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146

• Horn, B.K.P.: Robot Vision, McGraw-Hill, 1988, ISBN 0-07-030349-5
• Hlaváč, V., Šonka, M.: Počítačové vidění, Grada, 1993, ISBN 80-85424-67-3 
• Russ, J.C.: The IMAGE PROCESSING Handbook, CRC Press, 1995, ISBN 0-8493-2532-3
• Bass, M.: Handbook of Optics, McGraw-Hill, New York, USA, 1995, ISBN 0-07-047740-X

1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Hradiš 20.9.)
2. Statistical Pattern Recognition, Bayesian Clasifier and Mixture Models/Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 27.9.)
3. Scanning object detection, boosted classifiers, acceleration/Detekce objektů oknem, boostované klasifikátory, akcelerace (Zemčík 4.10.)
4. Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 11.10. slajdy1, slajdy2, slajdy3)
5. Object Detection - Trees, Random Forests, Yolo/Detekce objektů - Stromy, "Random Forests",Yolo (Juránek, 18.10. slajdy-en)
6. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 25.10. slajdy)
7. Hough Transform, RHT, RANSAC, Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování sekvencí (Hradiš, 1.11. slajdy1, slajdy2, slajdy2-en)
8. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 8.11. slajdy)
9. Invariant Image Regions/Invariantní oblasti obrazu (Beran, 15.11. slajdy)
10. Image Registration/Registrace obrazu (Čadík, 22.11. slajdy)
11. 3D Computer Vision/3D počítačové vidění (29.11. Šolony)
12. Stereovision, SLAM/Stereoviděni, SLAM (6.12. Šolony)
13. Conclusions, Hardware Accelerated Algorithms/Conclusions, Hardware Accelerated Algorithms (Zemčík 13. 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.

1. Homeworks (4-5 runs) at the beginning of semester
2. Individually assigned project for the whole duration of the course.

Homeworks, Mid-term test, individual project.