Topic outline

  • General

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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.ch

    Course 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

    Corentin Dumery (corentin.dumery@epfl.ch)

    Yingxuan You (yingxuan.you@epfl.ch)

    Zhantao Deng (zhantao.deng@epfl.ch)

    Emilien Seiler (emilien.seiler@epfl.ch)

    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, parts of the exam will be a follow-up to a previous exercise session. You will be allowed ONE double-sided hand-written (non-digital, non printed) A4 page of notes. It will count for 100% of your final grade. 

  • Course Schedule


     16-02-2026    Course
     23-02-2026  Course
     24-02-2026  Exercise Session 1 
     02-03-2026  Course
     09-03-2026  Course
     10-03-2026  Exercise Session 2 
     16-03-2026  Course
     23-03-2026  Course
     24-03-2026  Exercise Session 3 
     30-03-2026  Course
     06-04-2026  Holiday 
     13-04-2026  Course
     14-04-2026  Exercise Session 4 
     20-04-2026   Course
     27-04-2026  Course
     28-04-2026  Exercise Session 5 
     04-05-2026  Course
     11-05-2026  Course
     12-05-2026  Exercise Session 6
     18-05-2026  Course
     26-05-2026   Exercise Session 7




  • Reference Text Books

    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.


  • Introduction to the Class

  • From World to Images

    Available from 23 February 2026, 6:00 AM

  • Edge Detection

    Edge definition, edge operators, Canny edge detector, and machine-learning based detectors.

    Available from 2 March 2026, 6:00 AM

  • Delineation

    Going from edge elements to complete outlines. 

    Available from 9 March 2026, 6:00 AM

  • Segmentation

    Partitioning images into separate regions of interest.

    Available from 16 March 2026, 6:00 AM

  • Texture

    Texture: What is it and how can it be characterized and analyzed.

    Available from 23 March 2026, 6:00 AM

  • Shape from Shading and Texture

    Recovering 3D shape from one single image.

    Available from 30 March 2026, 6:00 AM

  • Shape from Stereo

    Recovering Depth from Multiple Images

    Available from 13 April 2026, 6:00 AM

  • Shape from Contours

    Recovering 3D shape from edges and occluding contours

    Available from 20 April 2026, 6:00 AM

  • Shape from Motion

    Recovering Shape from Video Sequences

    Available from 27 April 2026, 6:00 AM

  • Vision Applications

    Available from 4 May 2026, 6:00 AM

  • Summary

    Available from 11 May 2026, 6:00 AM

  • Exercise session 1

    Introduction to Python for Computer Vision

    Available from 24 February 2026, 6:00 AM

  • Exercise session 2

    Convolutions, image filters, gradients

    Available from 10 March 2026, 6:00 AM

  • Exercise Session 3

    TODO by Zhantao Deng

    You can find some previous examples in the section "Graded Exercise 1 - Mock Samples"


    Available from 24 March 2026, 6:00 AM

  • Exercise session 4

    General Hough Transform

    Available from 14 April 2026, 6:00 AM

  • Exercise Session 5

    K-Means Clustering for Image Segmentation, Image Sharpening

    Available from 28 April 2026, 6:00 AM

  • Exercise Session 6

    TODO by Corentin Dumery
    You can find some previous examples in the section "Graded Exercise 2 - Mock Samples"

    Available from 12 May 2026, 6:00 AM

  • Exercise Session 7

    Image Segmentation & Shape From Stereo

    Available from 26 May 2026, 6:00 AM