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Computer vision

Course code:INFOMCV
Credits:7.5 ECTS
Period:period 3 (week 6 through 15, i.e., 8-2-2021 through 16-4-2021; retake week 27)
Timeslot:C
Participants:up till now 0 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Ronald Poppe
tutorial group 1        Metehan Doyran
Note:No up-to-date course description available.
Text below is from year 2019/2020
Contents:The goal of computer vision is to allow computers to recognize and understand the world through visual information such as images and videos.

In this course, you will learn how cameras capture the 3D world, how to combine different views to reconstruct the world in 3D, how images can be described and how we can train algorithms to recognize what's depicted in images and videos. The course is centered around two main themes:
  • Multi-view reconstruction: construct a 3D object from multiple views
  • Classification with CNNs: classify videos with human actions using CNNs
Literature:May change!
Richard Szeliski, "Computer Vision: Algorithms and Applications", 2010 (download here for free). You can also order a paper copy from SV Sticky.
Course form:The course combines theory (lectures, tested during exam) and practice (assignments). The assignments are an important part of the course and final grade. Programming skills in C++ are required. For the second part, we will be using Python, but will provide sufficient materials and support.
Exam form:The final grade is average of the following assessments:
  • Assignments [50%]
  • Final exam [50%]
The minimum final grade to pass the course is 5.5. Assignments and final exam should each be completed with a minimum of grade 4.
Minimum effort to qualify for 2nd chance exam:To qualify for the retake exam, the grade of the original must be at least 4.
Description:Please contact Ronald Poppe for more information about the course.
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