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 CM13
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
Chen Zhao (chen.zhao@epfl.ch)
Aoxiang Fan (aoxiang.fan@epfl.ch)Corentin Dumery (corentin.dumery@epfl.ch)Deniz Mercadier (deniz.mercadier@epfl.ch)
Graded Exercise Sessions
We will grade two of the exercise sessions. They will count for 10% of you final grade each. There will be around two hours for you to implement some algorithms. You must join the graded exercise sessions in person, otherwise you will lose the points.
Recorded Lectures
The lectures will be recorded and deposited on this channel.Final exam
It will be a 90min closed book exam with multiple-choice and open-ended questions. You will be allowed ONE double-sided hand-written (non-digital, non printed) A4 page of notes. It will count for 80% of your final grade. -
17-02-2025 Course 24-02-2025 Course 25-02-2025 Exercise Session 1 03-03-2025 Course 10-03-2025 Course 11-03-2025 Exercise Session 2 17-03-2025 Course 24-03-2025 Course 25-03-2025 Exercise Session 3 (Cancelled grading) 31-03-2025 Course 07-04-2025 Course 08-04-2025 Exercise Session 4 GRADED 14-04-2025 Course 28-04-2025 Course 29-04-2025 Exercise Session 5 05-05-2025 Course 12-05-2025 Course 13-05-2025 Exercise Session 6 GRADED 19-05-2025 Course 20-05-2025 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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Recovering Shape from Video Sequences
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Introduction to Python for Computer Vision
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Convolutions, image filters, gradients
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K-Means Clustering for Image Segmentation, Image Sharpening
