CS543/ECE549: 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): Tuesdays 2-3PM, Thursdays 3-4PM

TAs: Lavisha Aggarwal (lavisha2), Hsiao-Ching Chang (hchang65), Wei Han (weihan3), Unnat Jain (uj2)
TA office hours (207 Siebel): Mondays 5-6PM, Wednesdays 12-1PM and 5-6PM, Fridays 1-2PM

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

Overview

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. MATLAB programming experience and previous exposure to image processing are highly desirable.

Recommended textbooks: Grading scheme: Academic integrity policy: You are encouraged to discuss assignments with each other, but coding and writing of reports must be done individually unless specifically instructed otherwise. You are also encouraged to search the Web for tips or code snippets, provided this does not make the assignment trivial and all external sources are explicitly acknowledged in the report. At the first instance of cheating (copying from other students or unacknowledged sources on the Web), a grade of zero will be given for the respective assignment or test. At the second instance, you will automatically receive an F for the entire course.

Syllabus

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 16 Introduction: PPT, PDF Homework: Assignment 0
Resource: MATLAB tutorial
January 18 Perspective projection: PPT, PDF Reading: F&P ch. 1
January 23 Cameras: PPT, PDF  
January 25 Light and shading: PPT, PDF Reading: F&P ch. 2
Assignment 0 due January 29, 11:59:59PM
January 30 Color: PPT, PDF Reading: F&P ch. 3
Homework: Assignment 1 MATLAB, Python
February 1 Linear filtering: PPT, PDF Reading: F&P ch. 4
February 6 Edge detection: PPT, PDF Reading: F&P sec. 5.1-5.2
February 8 Corner detection: PPT, PDF Reading: F&P sec. 5.3
Resource: Harris corner detector code
February 13 SIFT keypoints: PPT, PDF Reading: Distinctive image features from scale-invariant keypoints
February 15 Optical flow: PPT, PDF Reading: F&P sec. 11.1
Assignment 1 due February 19, 11:59:59PM
February 20 Fitting: PPT, PDF Reading: F&P sec. 10.2-10.4, 22.1
Homework: Assignment 2 MATLAB, Python
Project proposal
February 22 Hough transform: PPT, PDF Reading: F&P sec. 10.1
February 27 Alignment: PPT, PDF Reading: F&P sec. 12.1
March 1 Alignment cont.  
March 6 Camera calibration: PPT, PDF Reading: F&P ch. 1
March 8 Single-view modeling: PPT, PDF Reading: Ch. 2 from Hoiem and Savarese book
Assignment 2 and project proposals due March 12, 11:59:59PM
March 13 Epipolar geometry: PPT, PDF Reading: F&P sec. 7.1
Homework: Assignment 3 is out
March 15 Binocular stereo: PPT, PDF Reading: F&P ch. 7
March 27 Multi-view stereo: PPT, PDF  
March 29 Structure from motion: PPT, PDF Reading: F&P ch. 8
April 3 Intro to recognition: PPT, PDF  
April 5 Recognition cont.  
April 10 Deep learning: PPT, PDF  
April 12 Deep learning cont. Assignment 3 due April 12, 11:59:59PM
Project progress report due April 16, 11:59:59PM
April 17 Detection: PPT, PDF Homework: Assignment 4 is out
April 19 Detection cont.: PPT, PDF  
April 24 Segmentation: PPT, PDF  
April 26 CNNs for segmentation and beyond: PPT, PDF  
May 1 Selected project presentations Assignment 4 due May 2, 11:59:59PM
Final project reports due May 8, 11:59:59PM

Useful Resources

Tutorials, review materials

MATLAB reference