(c) Luigi Serafini

COMP 776: Computer Vision

Spring 2010, T TH 9:30-10:45, SN 115

Instructor: Svetlana Lazebnik  (lazebnik -at- cs.unc.edu)

Quick links: syllabus, schedule, useful resources

Overview

In the simplest terms, computer vision is the discipline of "teaching machines how to see." This field dates back more than forty years, but the recent explosive growth of digital imaging technology 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 strive to provide a unified perspective on the different aspects of computer vision, and give students the ability to understand 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 desirable, but not required.

Textbook: Computer Vision: A Modern Approach by David Forsyth and Jean Ponce is the recommended textbook for the course, though the instruction will follow this book very loosely. Another great resource is Richard Szeliski's textbook in progress, Computer Vision: Algorithms and Applications (draft available online).

Grading: Computer vision is a very hands-on subject. For this reason, the coursework will primarily consist of implementation (please make sure you have access to MATLAB with the Image Processing Toolbox installed). There will be three or four minor programming assignments and a larger final assignment which will most likely consist of a recognition competition (details to follow). Class participation will be another important component of the grade. This involves coming to class regularly, asking questions, and answering review questions. Without satisfactory participation, it will be impossible to get an "H" in the class. The weights assigned to different course components will be as follows:

Syllabus

I. Image formation II. Grouping and fitting III. Geometric vision IV. Recognition V. "Miscellaneous"

Schedule (tentative)

Date Topic Readings, assignments
January 12 What is computer vision? PPT, PDF Resource: MATLAB tutorial
January 14 Cameras: PPT, PDF Reading: F&P ch. 1
January 19 Cameras cont. Homework: Assignment 1 out
January 21 Radiometry: PPT, PDF Reading: F&P ch. 4, 5
January 26 Color: PPT, PDF Reading: F&P ch. 6
January 28 Linear filtering: PPT, PDF Reading: F&P ch. 7
February 2 Edge detection: PPT, PDF Reading: F&P ch. 8
Assignment 1 due at 5PM
February 4 Corner and blob detection: PPT, PDF Resource: Harris corner detector code
February 9 Blob detection cont. Homework: Assignment 2 out
February 11 Fitting, RANSAC: PPT, PDF Reading: F&P sec. 3.1, ch. 15
February 16 Hough transform: PPT, PDF  
February 18 Alignment: PPT, PDF Reading: Distinctive image features from scale-invariant keypoints
February 23 Alignment cont.
Single-view geometry: PPT, PDF
Reading: F&P ch. 2, 3
February 25 Single-view geometry cont.
Epipolar geometry: PPT, PDF
Reading: F&P sec. 10.1, ch. 11
Assignment 2 due at 5PM
March 2 Epipolar geometry cont.
Binocular stereo: PPT, PDF
Reading: F&P ch. 11
Homework: Assignment 3 out
March 4 Binocular stereo cont.
Multi-view stereo: PPT, PDF
 
March 16 Multi-view stereo cont.
Structure from motion: PPT, PDF
Reading: F&P sec. 12.3, 12.4, 13.3.1, 13.4, 13.5
March 18 Structure from motion cont.  
March 23 Intro to recognition: PPT, PDF Resource: ICCV 2009 Short Course on Object Recognition
Assignment 3 due at 5PM
March 25 Statistical recognition: PPT, PDF  
March 30 Bags of features: PPT, PDF Optional homework: Extra credit assignment out
April 1 Discriminative models: PPT, PDF Reading: F&P sec. 22.1, 22.2, 22.5
April 6 Generative models: PPT, PDF Homework: Final assignment out
April 8 Generative models cont.
Spatial models: PPT, PDF
 
April 13 Face detection: PPT, PDF
Eigenfaces (not covered in class): PPT, PDF
Reading: Robust Real-Time Face Detection
Reading: F&P sec. 22.3
April 15 Face detection cont. Extra credit assignment due at 5PM
April 20 Segmentation: PPT, PDF Reading: F&P ch. 14
April 22 Optical flow: PPT, PDF Final assignment due on Sunday, April 25th, 5PM
April 27 Optical flow concluded; final assignment discussion  

Useful Resources

Tutorials, review materials

General reference

MATLAB reference

The real world