Fall 2022 CS 543/ECE 549: Computer Vision

Quick links: schedule, Campuswire (announcements and discussion), Canvas (assignment submission and grades), Mediaspace (lecture recordings)

Instructor: Svetlana Lazebnik  (slazebni -at- illinois.edu)
Lectures: M W 11:00-12:15 1404 Siebel

TAs: Daniel McKee (dbmckee2), Zitong Zhan (zitongz3), Nan Liu (nanliu4), Nikash Walia (nikashw2), Hang Zhang (hangz2)

Instructor and TA office hours: see Campuswire

Contacting the course staff: For emergencies and special circumstances, please email the instructor. For questions about lectures and assignments, use Campuswire. For questions about your scores (including regrade requests), email the responsible TAs.


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.

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

Recommended textbooks: Grading scheme:
  • Programming assignments: 50%
    • Five MPs, done individually, in Python
  • Final project: 30%
    • Groups of two to four; deliverables include proposal, intermediate progress report, final report
  • Unit quizzes: 20%
    • Four multiple-choice online quizzes on the four units from the syllabus below
  • Participation: up to 3% extra credit
    • Students can get extra credit for actively participating in class, on Piazza, or during office hours
  Be sure to read the course policies!


I. Image processing and low-level vision
  • Image sampling, interpolation, transformations
  • Linear filters and edges
  • Feature extraction
  • Optical flow and feature tracking
II. Grouping and fitting
  • Least squares fitting, robust fitting
  • Alignment, image stitching
III. Image formation and geometric vision
  • Camera models
  • Light, shading and color
  • Camera calibration
  • Epipolar geometry
  • Two-view and multi-view stereo
  • Structure from motion
IV. Recognition and beyond
  • Statistical learning framework
  • Image classification
  • Deep learning
  • Object detection
  • Segmentation
  • Advanced topics (depending on time, student interest, and instructor choice): image generation and manipulation, deep learning for 3D vision, vision and language, video

Schedule (tentative)

Date Topic Readings (F&P 2nd ed.), assignments
August 22 Introduction: PPTX, PDF Self-study: See resources for Python and linear algebra tutorials, feel free to try U Mich EECS442 Mastery Assignment as a warmup
August 24 Image processing: PPTX, PDF  
August 29 Image processing cont. (slides above) Homework: Assignment 1
August 31 No lecture  
September 7 Image filtering: PPTX, PDF Reading: F&P ch. 4
September 12 Fourier analysis: PPTX, PDF Reading: Szeliski 3.4
Assignment 1 due
September 14 Edge detection: PPTX, PDF Reading: F&P sec. 5.1-5.2
September 19 Corner detection: PPTX, PDF Reading: F&P sec. 5.3
September 21 SIFT keypoint detection: PPTX, PDF Reading: Distinctive image features from scale-invariant keypoints
Homework: Assignment 2
September 26 Optical flow: PPTX, PDF Reading: F&P sec. 11.1
September 28 Fitting: PPTX, PDF Reading: F&P sec. 10.2-10.4, 22.1
October 3 Alignment: PPTX, PDF Reading: F&P sec. 12.1
October 5 Alignment cont. Assignment 2 due October 6
October 10 Cameras: PPTX, PDF Reading: F&P ch. 1
Project proposals due
October 12 Light and shading: PPTX, PDF Reading: F&P ch. 2
Homework: Assignment 3
October 17 Color: PPTX, PDF Reading: F&P ch. 3
October 19 Perspective projection: PPTX, PDF Reading: F&P ch. 1
October 24 Camera calibration: PPTX, PDF Reading: F&P ch. 1
October 26 Single-view modeling: PPTX, PDF  
October 31 Epipolar geometry: PPTX, PDF Reading: F&P sec. 7.1, H&Z ch. 9
Assignment 3 due
November 2 Structure from motion: PPTX, PDF Reading: F&P ch. 8
Homework: Assignment 4
November 7 Two-view stereo: PPTX, PDF Reading: F&P ch. 7
November 9 Two-view stereo cont. Project progress reports due November 11
November 14 Multi-view stereo: PPTX, PDF Assignment 4 due November 15
November 16 Light field modeling: PPTX, PDF Homework: Assignment 5
November 28 Recognition: PPTX, PDF  
November 30 Recognition cont.  
December 5 Project presentations Assignment 5 due December 6
December 7 Project presentations Final project reports due December 12