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Instructor: Charless Fowlkes Overview
Image understanding, extracting useful semantic content from image data, is a
core human ability whose emulation by machine systems has been an area
of active research in artificial intelligence for the last 40 years. Contemporary
computer vision research draws heavily from machine learning and serves as a
testing ground for new learning theories and algorithms. Computer vision in turn
provides a set of tools for many applications in multimedia information systems
and HCI, as well as the natural sciences, e.g. biomedical imaging. Graduate
students completing this course will be well prepared to comprehend current
research in computer vision or apply state-of-the-art techniques to problems of
interest in their own field.
Course Topics
Lecture Notes, Slides and ReadingsHomeworks
Textbook
The textbook for the course is Computer Vision: A modern approach,
by Forsyth and Ponce. We will not follow it closely but it will be valuable
for filling in details we don't discuss in class and providing an alternative
presentation.
Scribe Notes
In addition to the textbook, students in the class will participate in scribing
notes for the class. Each student will signup to produce draft notes for a given
lecture which will be distributed to the class after review by the instructor.
You can find the LaTeX template for the scribe notes here. Project IdeasGradingThe grading for this class will be
based on homeworks, participation (scribe notes) and a final project
HomeworksThere will be approximately 4 homeworks during the quarter. Each homework due at the beginning of class on the due date. Late homeworks will not be graded so please just hand in whatever you have completed by the beginning the class that it is due. Solution sketches will be provided after homeworks have been turned in. You will be required to use MATLAB for some of your homework problems. Academic HonestyHomeworks can be discussed, but each student must independently write up their own solutions. In particular, no sharing of code. Please see the university policy on academic honesty. It is fine to use reference materials found online, but do not search for homework solutions. Rather, students are strongly encouraged to ask questions at both office hours and on the class discussion group. |