Professor: Rajesh Rao office:  566 Allen Center email: rao at cs.washington.edu office hour: Tu 4:30pm-5:30pm

TA: Jiun-Hung Chen email: jhchen at cs.washington.edu office hours: Tu/Thu 1:30pm-3:00pm (Sieg 327)

The goal of computer vision is to understand the three-dimensional world from 2D images.  Problems include extracting interesting features from images, enhancing images, recognizing and locating objects in images, estimating the motion of objects in video sequences, and computing 3D properties of the world from images.  This course will provide an introduction to computer vision, including topics such as feature detection, pattern recognition, visual learning, object recognition, image segmentation, image stitching and mosaicing, motion estimation, color, texture, and applications such as content-based image retrieval, tactile graphics, and computer vision for Mars exploration.

Note about office hours:  If it's impossible for you to make it to scheduled office hours, you can usually arrange an appointment with the TA or instructor. Just send an email requesting a meeting.

Prerequisites

• Data structures
• A good working knowledge of C and C++ programming
• Linear algebra
• Vector calculus
• No prior knowledge of vision is assumed.

Textbook

Shapiro and Stockman, Computer Vision, Prentice-Hall, 2001.

Administrative

• Email List:  First get a password reminder (use the Edit Options button at the very bottom of the page), then use the hypermail archive online.
• Computer Accounts:  if you don't have a CSE account, click here

Grading

• Programming Projects (80%)
• Final (20%)
• Late projects will be penalized by 33% for each day it is late, and no extra credit will be awarded.  A project due on Friday morning that is turned in on Monday night or Tuesday morning will count two days late (weekends do not count).

Syllabus (tentative)

Images and Image Processing (Chaps. 1, 2, 5)

Image Features and Interest Operators (Lecture notes)

Image Stitching and Mosaics (Lecture notes)

Human Vision (Lecture notes)

Pattern Recognition and Visual Learning (Chap. 4)

Color (Chap. 6)

Texture (Chap. 7)

Content-Based Image Retrieval (Chap. 8)

Motion Estimation (Chap. 9)

Segmentation (Chap. 10)

Object Recognition (selected topics from Chaps. 11 & 14)

3D and Stereo Vision (selected topics from Chaps. 12 and 13)

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Last modified: January 2, 2009