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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-requisits. 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 and numpy computing environment, 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, OD Ondřej Drbohlav,MS Milan Šulc
Lectures will be given via zoom, link: https://feectu.zoom.us/j/93818244152, according the schedule, as listed below (from 11:00, zoom meeting opened at 10:50 )
* Update of course slide material
* I forgot to start recording of the second lecture, I am sorry. The provided recording is from the last year. Note that the slides has changed slightly. Semantic segmentation architectures and deep fakes were added this year. Minor errors were fixed in the slides.
Work during the semester 50%, written part of the exam 40%, oral part of the exam 10%
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.