CS 4501: Computer Vision
Spring 2011
|
Home |
Schedule |
Assignments |
Final Project |
Additional Resources
Course Summary
Lectures: MW 3:30-4:45 PM, Room: MEC 341
Instructor: Jason Lawrence - 212 Olsson (office hours: MW 1:30PM-3PM)
Teaching Assistant: Sean Arietta - 227 Olsson, Seat 4 (office hours: MW 9AM-11AM)
Announcements
Final project presentations are scheduled to take place on Monday, May 2 from 6PM-9PM in MEC 341.
Course Description
Computer vision is a subfield of computer science that focuses on
extracting useful information from images and videos. Examples of
"useful information" include detecting the presence and identify of
human faces in a photograph, recovering the 3D geometry of the objects
in a photograph, and tracking and recognizing different types of
motion in a video sequence. Computer vision algorithms have found a
wide range of applications from 3D laser scanning systems used in
manufacturing, city planning, entertainment, forensics, etc., to
computer interfaces accessible to people with physical impairments,
and autonomous navigation systems like those developed through the
recent DARPA grand
challenges.
This course serves as an introduction to computer vision and consists
of lectures and hands-on programming assignments in MATLAB. Prior experience with MATLAB is not required, although
students are expected to have completed and done well in CS2150 and
have some background in linear algebra. Experience with signal
processing, statistics, and computer graphics will also be useful, but
not necessary. Specific topics include:
- Principles of image formation
- Edge and feature detection
- Segmentation and clustering
- Feature recognition
- Feature tracking
- Optical flow
- Camera calibration
- Stereo-based scene reconstruction
- Photometric stereo
- Image-based rendering and modeling
Prerequisites
The only prerequisite is CS 2150. This course will require
programming (primarily in MATLAB, although you are welcome to do all of the assignments in C/C++), as well as some background in
data structures and linear algebra. Experience with signal
processing, statistics, and/or computer graphics is useful but not
necessary.
Textbook
The recommended textbook for this course is Computer Vision: A Modern Approach by
Forsyth and Ponce. A few copies are on reserve in the Brown Science and Engineering Library. If you'd like to buy your very own copy I recommend Amazon.
There are a number of other vision textbooks that you may also find useful:
- Introductory Techniques for 3-D Computer Vision by Emanuele
Trucco and Alessandro Verri (also on reserve at Brown).
- Computer Vision: Algorithms and Applications by Richard Szeliski. Available on-line.
- Computer Vision by Linda Shapiro and George Stockman (on reserve at Brown).
- Computer Vision by Dana Ballard and Christopher Brown (available on-line, although slightly outdated at this point).
- Multiple View Geometry by Richard Hartley and Andrew Zisserman (on reserve at Brown).
- Pattern Classification by Richard Duda, Peter Hart, and David Stork (on reserve at Brown).
- Machine Learning by Tom Mitchell (on reserve at Brown).
The schedule reports how the first three textbooks relate to the material covered in lecture. We will occasionally use research papers and on-line materials to supplement the lectures.
Grading
There will be four programming assignments each worth 17.5% of your
grade and a final project worth 30%.
Acknowledgements
Many thanks to Szymon Rusinkiewicz for generously sharing many of the slides and assignments used in Princeton's COS426 course.
