ETH Zurich - D-INFK - IVC - CVG - Lectures - Computer Vision - Course 2018

Course 2018

Instructors:Marc Pollefeys, Luc Van Gool, Vittorio Ferrari
Teaching assistants: CVG part: Peidong Liu, Nikolay Savinov, Pablo Speciale,
        Katarina Tóthová, Viktor Larsson,
CVL part: Yawei Li, Vaishakh Patil, Andrii Ihnatov,
        Stamatios Georgoulis, Bhaskara Rao Chintada
Lectures: Wednesdays from 13:15-16:00 in CHN C 14
Exercises: Thursdays from 15:15-16:00 in CHN C 14


263-5902-00L Computer Vision

No exercise session on 20.09.2018
No exercise session on 25.10.2018
No exercise session on 20.12.2018

Computer Vision (following Tomaso Poggio, MIT): Computer Vision, formerly an almost esoteric corner of research and regarded as a field of research still in its infancy, has emerged to a key discipline in computer science. Vision companies have emerged and commercial applications become available, ranging from industrial inspection and measurements to security database search, surveillance, multimedia and computer interfaces. Computer Vision is still far from being a solved problem, and most exciting developments, discoveries and applications still lie ahead of us. Understanding the principles of vision has implications far beyond engineering, since visual perception is one of the key modules of human intelligence.

Important: course is managed through moodle

All the lectures and exercises will be posted on moodle. You also have to submit the exercises there. If you have questions, there is a forum on moodle. You can also ask private questions to TAs through the moodle system. Please, don't email questions unless it's very urgent.

Course Objectives

The objectives of this course are:
1.To introduce the fundamental problems of computer vision.
2.To introduce the main concepts and techniques used to solve those.
3.To enable participants to implement solutions for reasonably complex problems.
4.To enable participants to make sense of the computer vision literature.

Course Topics

Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition

Target Audience

The target audience of this course are Master students, that are interested to get a basic understanding of computer vision.


Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, basic programming skills in Matlab and C.

Some useful links

The Computer Vision Homepage
Middlebury Stereo Vision Page
VLFeat SIFT package for MATLAB
Course Notes
Computer Vision: Algorithms and Applications


Q1: I am re-taking Computer Vision class. Can you transfer exercise grades from the previous time?
A: Please write directly to the TA responsible for the particular assignment INSTEAD of sending the assignment one more time. If the exercises did not change, the score will be transferred.
Q2: My code is correct, it just happens it does not run. Can you grade it?
A: No, please make sure it runs in other environment before sending. We will only grade working code.
Q3: My code is correct, but I did not write the report. Can you grade the assignment?
A: No, you should write the report.

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