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 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: Monday 13:15 - 15:00 CM3
Exercises: Tuesday 10:15 - 12:00 every other week. INM 200 (A-M), INM 202 (N-Z)
Please check the course schedule and bring your own laptops for the exercise sessions.
Questions
If you have any questions please post them in the discussion forum and we will answer you.
Contact TAs
If you have any questions please email one of the TAs and we can arrange a meeting.
Andrey Davydov (andrey.davydov@epfl.ch)
Benoît Guillard (benoit.guillard@epfl.ch)
Michal Jan Tyszkiewicz (michal.tyszkiewicz@epfl.ch)
Chen Zhao (chen.zhao@epfl.ch)
Zhen Wei (zhen.wei@epfl.ch)
Corentin Dumery (corentin.dumery@epfl.ch)
Jean Decroux (jean.decroux@epfl.ch)
Graded Exercise Sessions
We will grade two of the exercise sessions. They will count for 10% of you final grade each.
Recorded Lectures
The recorded lectures from the 2020/2021 school year will be available on the webpages.
Final exam
It will be a 90min closed book exam with multiple-choice and open-ended questions. You will be allowed ONE hand-written A4 page of notes. It will count for 80% of your final grade. The exam will take place on June 20th, starting at 9:15 in CE1 and CE1 515. -
20-02-2023 Course 27-02-2023 Course 28-02-2023 Exercise Session 1 06-03-2023 Course 13-03-2023 Course 14-03-2023 Exercise Session 2 20-03-2023 Course 27-03-2023 Course 28-03-2023 Exercise Session 3 GRADED 03-04-2023 Course 10-04-2023 No Course (holiday - Lundi de Pâques) 17-04-2023 Course 18-04-2023 Exercise Session 4 24-04-2023 Course 01-05-2023 Course 02-05-2023 Exercise Session 5 08-05-2023 Course 15-05-2023 Course 16-05-2023 Exercise Session 6 GRADED 22-05-2023 Course 29-05-2023 Course 30-05-2023 Exercise Session 7 -
R. Szeliki, Computer Vision: Computer Vision: Algorithms and Applications, 2021.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2003.
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Edge definition, edge operators, Canny edge detector, and machine-learning based detectors.
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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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Convolutions, image filters, gradients
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General Hough Transform
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K-Means Clustering for Image Segmentation, Image Sharpening
