CS543/ECE549 Computer Vision (Spring 2024)

Instructor: Saurabh Gupta
TAs: David Yao(dyyao2), Refik Mert Cam (rcam2), Xiaodan Hu (xiaodan8), Shun-Hsiang Hsu (hsus2)
Lecture Times: 09:30AM - 10:45AM on Wednesdays and Fridays
Lecture Location: 1404 Siebel Center For Computer Science
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To the extent possible, you should use Campuswire for asking questions. If you have a logistics question specific to yourself, you can email the staff at cv-sp24-staff@lists.illinois.edu. If you have a question or concern that you only want to share with the instructor, please email at saurabhg@illinois.edu.

Course Description

In the simplest terms, computer vision is the discipline of ‘teaching machines how to see.’ This field dates back more than fifty years, but the recent explosive growth of digital imaging and machine learning technologies makes the problems of automated image interpretation more exciting and relevant than ever. There are two major themes in the computer vision literature: 3D geometry and recognition. The first theme is about using vision as a source of metric 3D information: given one or more images of a scene taken by a camera with known or unknown parameters, how can we go from 2D to 3D, and how much can we tell about the 3D structure of the environment pictured in those images? The second theme, by contrast, is all about vision as a source of semantic information: can we recognize the objects, people, or activities pictured in the images, and understand the structure and relationships of different scene components just as a human would? This course will provide a coherent perspective on the different aspects of computer vision, and give students the ability to understand state-of-the-art vision literature and implement components that are fundamental to many modern vision systems.


Basic knowledge of probability, linear algebra, and calculus. Python programming experience and previous exposure to image processing are highly desirable.

Recommended Textbooks

  1. Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2nd ed.)

  2. Computer Vision: Algorithms and Applications by Richard Szeliski (PDF available online)

  3. Multiple View Geometry in Computer Vision by Richard Hartley and Andrew Zisserman

Grading Scheme

Tentative and subject to changes in the first few weeks.

  1. Unit quizzes: 20%. Four quizzes in CBTF on the different topics from the syllabus.

  2. Programming assignments: 55%. Five MPs @11% each, done individually, in Python.

  3. Final project: 25%. In groups.

  4. Participation: up to 1% extra credit. Students can get extra credit for actively participating in class and on Campuswire.


  1. Enrollment information for graduate students: Graduate student enrollment works on a first come first serve basis for this class, and I am unable to make exceptions.

  2. Enrollment information for undergraduates: Please fill up the following Google form here (you will need to sign in with your Google Apps @ Illinois account to access it), and we will grant you with a grad override if we believe you have the right preparation for taking this graduate class. The override should allow you to sign up should capacity become available in class.


This course is largely based on Prof. Svetlana Lazebnik's Computer Vision course. We would like to thank her and the many researchers who have made their slides and course materials available.