Search
Schedule on FEL (CZ course) Schedule on FEL (EN course) Upload system Discussion forum Discussion forum archive (2022/2023) Labs
This course focuses on the following computer vision problems: finding correspondences between images using image features and their robust invariant descriptors, image retrieval, object detection and recognition, and visual tracking.
The course has no formal pre-requisites. However, certain skills and knowledge are assumed, and it is the responsibility of the student to get to the required level.
The assignments are implemented in the Python, numpy, pytorch computing environment, mostly in form of jupyter notebooks, and familiarity with it will help. The programing assignments, involving either implementing, modifying or testing computer vision methods, are a substantial part of the labs.
Knowledge of the basics of digital image processing as convolution, filtration, intensity transformations, image function interpolations and basic geometric transformations of the image (see the first lab) is assumed. Knowledge of linear algebra and probability theory is needed to understand the presented computer vision methods.
Lecturers: JM Jiří Matas, JC Jan Čech, DM Dmytro Mishkin, GT Giorgos Tolias, MS Milan Šulc
Note: some of the lectures have changed, but the 2021 recordings mostly provide a good idea about the content. Lectures will be streamed on YouTube, live link: https://www.youtube.com/playlist?list=PLQL6z4JeTTQkqF6KkcZZDi2KFwky9SQpq Recorded lectures - playlist: https://www.youtube.com/playlist?list=PLQL6z4JeTTQnozHfghnzq3AK-lIBZmQdP Recorded lectures - playlist (2023): https://www.youtube.com/playlist?list=PLQL6z4JeTTQneuiXekoB639gEOzuen79_
Changes compared to the last year are in italics.
Work during the semester 50%, written part of the exam 40%, oral part of the exam 10%.
Note that the points from the labs are reweighted using a “normalization factor” so that they correspond to 50% of your evaluation. That means, the points from the labs that contribute to your exam, are (your total number of points from semester including bonus points)/(sum of points available from all non-bonus tasks) * 50.
(your total number of points from semester including bonus points)/(sum of points available from all non-bonus tasks) * 50
Examples of exam questions. There will be 4-5 similar questions at the written part of the exam. The oral part of the questions takes place after the written part and will focused on discussion of your answers.
Lecture slides constitute the main source of study literature in this course.
Further information is available in next sections of this page. We would appreciate your feedback on the contents and organization on the discussion forum of the course.
Good luck to all participants of the course.
Consultations are possible upon request.