Overview

This course is an introduction to fundamental and advanced topics in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2D and 3D object representation, object recognition, vision systems and applications.

Announcements

• Unfortunately, the course staff cannot sign your add forms and will not be able to respond to emails about this
• The course wait list is managed by the computer science department. The course is currently capped at 120 students. We are trying to request a larger room.

Course Policy

• Homework must be turned in by the posted deadline. One minute late is late. There are absolutely no extensions.
• You may work in groups, but homework must be written up individually.
• Academic dishonesty will result in a zero for the full course and your case will be sent to the dean's office.

Course Materials

We do not require a textbook. However, you may find the following books are useful resources:

• Computer Vision: A Modern Approach by Forsyth and Ponce
• Computer Vision Algorithms and Applications by Szeliski
• Multiple View Geometry in Computer Vision by Hartley and Zisserman
• Machine Learning: A Probabilistic Perspective by Murphy

We gratefully acknowledge several instructors for course material and slides: Shree Nayar, Antonio Torralba, William Freeman, Deva Ramanan, Kristen Grauman, Alyosha Efros, James Hays, Fei-Fei Li, Jia Deng.