The goal of computer vision is to compute properties of the three-dimensional world from digital images.  Problems in this field include identifying the 3D shape of an environment, determining how things are moving, and recognizing familiar people and objects, all through analysis of images and video.  This course provides an introduction to computer vision, including such topics as feature detection, image segmentation, motion estimation, image mosaics, 3D shape reconstruction, and object recognition.

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

Prerequisites

• Data structures (CSE 326)
• A good working knowledge of C and C++ programming
• Linear algebra
• Vector calculus
• No prior knowledge of vision is assumed.

Textbooks

We will use readings from a few textbooks.

Administrative

• Email List:  Send an email to majordomo@cs.washington.edu with "subscribe cse490CV" as the text of the body of the email.  Also check the hypermail archive online.
• Computer Accounts:  if you are a non-CSE major, click here.

Grading

• Programming Projects (70%)
• Midterm (15%)
• Final (15%)
• You are permitted a total of three late days for the quarter that may be used on any of the programming projects. After that, the grade on the late project will be reduced by 33% for each day it is late, and no extra credit will be awarded.  A project due on Thursday night that is turned in on Monday morning will cost 2 late days (weekends do not count).

Syllabus (tentative)

Image Filtering (2 weeks)

• filtering, convolution
• edge detection
• feature detection (corners, lines)
• hough transform
• image pyramids

Image Transformation (2 weeks)

• image warping (parametric transformations, resampling, texture mapping)
• image compositing (alpha blending, color mosaics)
• segmentation and matting (snakes, scissors)

Motion Estimation (2 weeks)

• optical flow
• image alignment
• image mosaics
• feature tracking

3D Modeling (3 weeks)

• projective geometry
• camera modeling
• single view metrology
• camera calibration
• stereo
• structure from motion

Object Recognition and Applications (1 week)

• eigenfaces
• applications (graphics, robotics)

Last modified 02/05/2002