Course detail

Computer Vision (in English)

FIT-POVaAcad. year: 2026/2027

Introduction to the principles and methods of computer vision: image formation and scene geometry, image features and their matching, geometric scene reconstruction, motion analysis and localization, recognition and segmentation of visual content, learning of visual representations, connecting vision and language, 3D computer vision methods, video analysis, and open problems in computer vision.

Language of instruction

English

Number of ECTS credits

5

Assignment to study programme types

Master's

Mode of study

Not applicable.

Guarantor

prof. Dr. Ing. Pavel Zemčík, dr. h. c.

Department

Department of Computer Graphics and Multimedia (UPGM)

Offered to foreign students

Of all faculties

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

Two home assignments, mid-term test and individual project.

Aims

Students will gain a comprehensive overview of the principles and methods of computer vision, ranging from geometric approaches to current methods based on neural networks. They will understand the process of scene capture, camera calibration and the reconstruction of 3D information, and will apply this knowledge in practice in an individual homework assignment. They will become familiar with representations of image data and with modern approaches to object detection and recognition. In a practical assignment, they will build their own dataset and train their own object detector. They will further apply the acquired knowledge in a team project on a selected topic. At the same time, they will improve their skills in working with image processing and machine learning tools in Python, in data preparation and annotation, and in the evaluation of experimental results.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edition, Cambridge University Press, 2004, ISBN 978-0-521-54051-3
Prince, S.J.D.: Understanding Deep Learning, The MIT Press, 2023, ISBN 978-0-262-04864-4
Szeliski, R.: Computer Vision: Algorithms and Applications, 2nd edition, Springer, 2022, ISBN 978-3-030-34371-2
Torralba, A., Isola, P., Freeman, W.T.: Foundations of Computer Vision, The MIT Press, 2024, ISBN 978-0-262-04897-2 

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme MIT-EN Master's 0 year of study, winter semester, elective

  • Programme IT-MGR-1H Master's

    specialization MGH , 0 year of study, winter semester, recommended course

  • Programme MITAI Master's

    specialization NHPC , 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 NISY , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 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, profile core courses
    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 NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, compulsory, profile core courses

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. Michal Španěl, Ph.D.
Ing. Marek Šolony, Ph.D.
prof. Dr. Ing. Pavel Zemčík, dr. h. c.
doc. Ing. Vítězslav Beran, Ph.D.
prof. Ing. Martin Čadík, Ph.D.
Ing. Marek Vaško
Ing. Michal Hradiš, Ph.D.
Ing. Roman Juránek, Ph.D.

Syllabus

  1. Introduction, motivation and applications/Úvod, základy, motivace a aplikace (Zermčík 15.9.)
  2. Scanning object detection, boosted classifiers, acceleration/Detekce objektů oknem, boostované klasifikátory, akcelerace (Zemčík 22.9.)
  3. Statistical Pattern Recognition, Bayesian Clasifier and GMM/Statistické rozpoznávání, Bayesovský klasifikátor a GMM (Španěl 29.9.)
  4. Clustering and Image Segmentation / Shlukování a segmentace obrazu (Španěl 6.10. slajdy1, slajdy2, slajdy3)
  5. Object Detection - Trees, Random Forests, Yolo/Detekce objektů - Stromy, "Random Forests",Yolo (Juránek, 13.10. slajdy-en)
  6. Convolutional Neural Networks and Automatic Image Tagging/Konvoluční neuronové sítě a tagování obrazu (Hradiš, 20.10. slajdy)
  7. Hough Transform, RHT, RANSAC, Sequence Processing/Houghova transformace, RHT, RANSAC, zpracování sekvencí (Hradiš, 27.10. slajdy1, slajdy2, slajdy2-en)
  8. 3D Computer Vision/3D počítačové vidění (3.11. Šolony)
  9. Test, Stereovision, SLAM/Stereoviděni, SLAM (10.11. Šolony)
  10. Analysis and Feature Extraction from Images/Analýza a extrakce příznaků z textur (Čadík 24.11. slajdy)
  11. Image Registration/Registrace obrazu (Čadík, 1.12. slajdy)
  12. Invariant Image Regions/Invariantní oblasti obrazu, conclusions (Beran, 8.12. slajdy, Zemčík)

NOTE: The topics and dates are just FYI, not guaranteed, and will be continuously updated.

Project

26 hours, compulsory

Teacher / Lecturer

Ing. Martin Kostelník
Ing. Michal Hradiš, Ph.D.

Syllabus

The project work in the course consists of two parts:

  1. (10 pts) Two individual homework assignments. In the first half of the semester, students calibrate a camera and project a 3D animation into real video (augmented reality). In the second half of the semester, they create their own dataset and train their own object detector.
  2. (30 pts) A team project for 2–3 students on selected topics. After consultation, students may propose a topic of their own. The results will be presented during a poster session at the end of the semester. Projects from past years can be seen here: https://www.fit.vut.cz/person/ikostelnik/public/knn-pova-projects/.

Elearning

eLearning: currently opened course