Spring 2019 CS 543/ECE 549: Computer Vision

Quick links: schedule, lecture videos (choose Log In Via Institution), Piazza (announcements and discussion), Compass (assignment submission and grades)

Instructor: Svetlana Lazebnik  (slazebni -at- illinois.edu)
Lectures: T TH 11:00-12:15, 1310 DCL
Instructor office hours (3308 Siebel): T TH 2-3PM

TAs: Maghav Kumar (mkumar10), Medhini Narasimhan (medhini2), Xinke Deng (xdeng12), Shuai Tang (stang30), Zhe Xu (zhex2), Wilfredo Torres Calderon (trrscld2)
TA office hours: M W F 11AM-12PM, 2-3PM, by the whiteboard outside 3407 Siebel

Always check announcements on Piazza for short-notice changes to instructor and TA office hours!


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 (preferred) or MATLAB programming experience and previous exposure to image processing are highly desirable.

Recommended textbooks: Grading scheme:   Be sure to read the course policies!


I. Image formation and low-level vision II. Grouping and fitting III. Geometric vision IV. Recognition and beyond

Schedule (tentative)

Date Topic Readings (F&P 2nd ed.), assignments
January 15 Introduction: PPTX, PDF Homework: Assignment 0
January 17 Perspective projection: PPTX, PDF Reading: F&P ch. 1
January 22 Cameras: PPTX, PDF  
January 24 Light and shading: PPTX, PDF Reading: F&P ch. 2
Assignment 0 due January 25, 11:59:59PM
January 29 Color: PPTX, PDF Reading: F&P ch. 3
Homework: Assignment 1
January 31 Linear filtering: PPTX, PDF Reading: F&P ch. 4
February 5 Edge detection: PPTX, PDF Reading: F&P sec. 5.1-5.2
February 7 Corner detection: PPTX, PDF Reading: F&P sec. 5.3
February 12 SIFT keypoints: PPTX, PDF Reading: Distinctive image features from scale-invariant keypoints
Assignment 1 due February 11, 11:59:59PM
February 14 Optical flow: PPTX, PDF Reading: F&P sec. 11.1
Homework: Assignment 2, project proposal
February 19 Fitting: PPTX, PDF
No class -- watch video on Echo360
Reading: F&P sec. 10.2-10.4, 22.1
February 21 Hough transform: PPTX, PDF
No class -- watch video on Echo360
February 26 Alignment: PPTX, PDF
Reading: F&P sec. 12.1
February 28 Alignment cont.  
March 5 Camera calibration: PPTX, PDF
Reading: F&P ch. 1
Assignment 2 due March 4, 11:59:59PM
March 7 Single-view modeling: PPTX, PDF
Reading: Ch. 2 from Hoiem and Savarese book
Project proposals due March 11, 11:59:59PM
March 12 Epipolar geometry: PPTX, PDF
Reading: F&P sec. 7.1
Homework: Assignment 3
March 14 Structure from motion: PPTX, PDF
Reading: F&P ch. 8
March 26 Two-view stereo: PPTX, PDF
Reading: F&P ch. 7
March 28 Multi-view stereo: PPTX, PDF
April 2 Intro to recognition: PPTX, PDF
April 4 Neural networks: PPTX, PDF
April 9 Deep convolutional networks: PPTX, PDF
Assignment 3 due April 8, 11:59:59PM
April 11 Deep convolutional networks cont. Homework: Assignment 4
April 16 Detection: PPTX, PDF
Project progress reports due April 15, 11:59:59PM
April 18 Detection cont.  
April 23 Segmentation: PPTX, PDF
April 25 CNNs for segmentation and beyond: PPTX, PDF
April 30 Selected project presentations Assignment 4 due May 1, 11:59:59PM
Final project reports due May 6