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Computer vision

Website:website containing additional information
Course code:INFOMCV
Credits:7.5 ECTS
Period:period 3 (week 6 through 15, i.e., 4-2-2019 through 12-4-2019; retake week 27)
Timeslot:C
Participants:up till now 61 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Tue 13.15-15.007 BESTUURS-LIEREGG Ronald Poppe
8 BBG-209
9-11 BESTUURS-LIEREGG
12 BBG-209
13-14 BESTUURS-LIEREGG
Thu 11.00-12.456 BBG-201
7-14 RUPPERT-042
tutorial group 1        Alexandros Stergiou
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
  • Action recognition: classify novel videos using CNNs


Programming skills in C/C++ are required. This course emphasizes the practicality of computer vision, meaning more projects, which are more fun.
Literature:Richard Szeliski, "Computer Vision: Algorithms and Applications", 2010 (download here)
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. We will also be using Python. We provide support throughout the course.
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.
Minimum effort to qualify for 2nd chance exam:To participate in the retake exam, your weighted grade needs to be at least 4.
Description:Please contact Ronald Poppe for more information about the course.
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