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This course is devoted to computer vision problems: Finding of correspondences between images using image features and their robust invariant descriptors, features matching, picture stitching, object and segment recognition in pictures or video, image retrieval and object tracking in the video sequences.
The computer vision methods course expects programming skills in Python and numpy computing environment. The programing assignments solving various computer vision methods are a substantial part of the labs. The attendee is expected to know basics of digital image processing as convolution, filtration, intensity transformations, image function interpolations and basic geometric transformations of the image (see the first lab). The attended is also expected to govern basics of linear algebra and probability theory.
Lecturers: JM Jiří Matas, JC Jan Čech, OD Ondřej Drbohlav, MS Milan Šulc
* update of course slide material
Plant recognition | Case study: Plant recognition using Deep Nets |
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.