CS651: Computer Vision
Fall 2007
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Course Summary
Lectures: T,Th 12:30-1:45 PM, Room: MEC 216
Instructor: Jason Lawrence - 212 Olsson (office hours: WF 3-5 and by appointment)
Announcements
The fourth assignment has been posted.
Course Description
This course serves as an introduction to 2-D and 3-D computer vision.
Topics include: principles of image formation; edge and feature
detection; segmentation and clustering; feature recognition; feature
tracking and optical flow; camera calibration; stereo-based scene
reconstruction; photometric stereo; and image-based rendering and
modeling.
Prerequisites
The only prerequisite is CS216. This course will require programming
(in C, C++, and/or Matlab), 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
Introductory Techniques for 3-D Computer Vision by Emanuele
Trucco and Alessandro Verri. We will also read several research papers.
Grading
There will be four programming assignments each worth 17.5% of your
grade and a final project worth 30%.
Acknowledgements
The instructors wish to thank Szymon Rusinkiewicz for generously sharing the lecture slides and programming assignments used in Princeton's COS426 course.
