Computer Vision
Topic outline
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Welcome to the Computer Vision class!
Computer Vision is the branch of Computer Science whose goal is to model the real world or to recognize objects from digital images. These images can be acquired using still and video cameras, infrared cameras, radars, or specialized sensors such as those used in the medical field.
The students will be introduced to the basic techniques of the field of Computer Vision. They will learn to apply Image Processing techniques where appropriate.
We will concentrate on the black and white and color images acquired using standard video cameras. We will introduce the basic processing techniques, such as edge detection, segmentation, texture characterization, and shape recognition.Instructor
Prof. Pascal Fua
Computer Vision Laboratory (CVLAB)
BC 310
E-mail: pascal.fua@epfl.chCourse Times and Locations
Lectures: Friday 13:15 - 15:00 (INM 202)
Exercises: Every other Tuesday 10:15 - 12:00 (INM 202) - please bring your own laptops for the exercise sessions.
Office Hours
If you have any questions please email one of the TAs and we can arrange a meeting.
Semih Günel (semih.gunel@epfl.ch)
Isinsu Katircioglu (isinsu.katircioglu@epfl.ch)
Udaranga Wickramasinghe (udaranga.wickramasinghe@epfl.ch)
Final Exam
It will be a 90min closed book exam and 80% of your grade will come from this exam. You will be allowed ONE hand-written A4 page of notes. Here is a sample exam from a previous year.
Graded Exercises
Not all practical sessions will be graded. However, two of them will be, one in the middle of the term and another towards the end. This will count for the remaining 20% of your grade.
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No Course (EPFL Holiday)
22.02.2019
Course
26.02.2019
Exercise Session
01.03.2019
Course
08.03.2019
Course
12.03.2019
Exercise Session
15.03.2019
Course
22.03.2019
Course
26.03.2019
Exercise Session
29.03.2019
Course
05.04.2019
Course
09.04.2019
Graded Exercise Session
12.04.2019
Course
19.04.2019
No Course (holiday)
23.04.2019
No Exercise Session (holiday)
26.04.2019
No Course (holiday)
30.04.2019
Exercise Session
03.05.2019
Course
10.05.2019
Course
14.05.2019
Exercise Session
17.05.2019
Course
24.05.2019
Course
28.05.2019
Graded Exercise Session
31.05.2019
Course
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R. Szeliki, Computer Vision: Computer Vision: Algorithms and Applications, 2010.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2003.
M. Nielsen, Neural Networks and Deep Learning, 2015.
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Edge definition, edge operators, Canny edge detector, and parametric matching.
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Going from edge elements to complete outlines.
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Partitioning images into separate regions of interest.
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Texture: What is it and how can it be characterized and analyzed.
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Recovering 3D shape from one single image.
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Recovering Depth from Multiple Images
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Recovering 3D shape from edges and occluding contours
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Introduction to Python for Computer Vision
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Image filtering and edge detection