Computer Vision
    CS6670, Spring 2011

Time: M/W 1:25pm - 2:40pm
Place: Phillips 101 (map)

Instructor: Noah Snavely (
    Office: Upson 4157, (607) 255-4820
    Office Hours: Wednesdays 3-4:30pm

TA: Song Cao (
    Office: Upson 4144
    Office Hours: Tuesdays 4 pm - 5 pm, Fridays 1:30 pm - 2:30 pm

Newsgroup: cornell.class.cs6670

  Lectures Projects  

The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos.

This course will provide an introduction to computer vision, including such topics as image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. This is a project-based course, in which you will implement several computer vision algorithms and do a final project on a research topic of your choice.

This course will be self-contained; students do not need to have computer vision background. This course will assume a reasonable knowledge of linear algebra as a prerequisite. The programming assignments will be in C++, so a familiarity with these languages is essential.
Please send me email or speak to me if you are unsure of whether you can take the course.

This course will have readings from Computer Vision: Algorithms and Applications, by Richard Szeliski. An online version is available, or you may purchase the book at a variety of locations.

Academic Integrity
This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit must be the student's own work. Violations of the rules will not be tolerated.