Fridays 1-4 pm
Unique # 55910
Instructor: Kristen Grauman
Office hours: by appointment, CSA 114 (modular building in front of ENS)
Talk by David Lowe on Monday April 28, 12:00 pm, SEA 4.244, “Object Recognition from Invariant Local Features”
10-minute project presentations will be given in class on May 2. Please send in any slides you will use by May 1.
This is a graduate seminar course in computer vision. We will survey and discuss current computer vision literature, mainly relating to object recognition and content-based retrieval for images and videos. The goals of the course will be to understand current approaches to some important problems, to actively analyze their strengths and weaknesses, and to identify interesting open questions and possible directions for future research.
Topics will include
· image/video search and the web
· recognition models
· learning similarity measures
· fast indexing methods
· data mining with text and images
· computational photography
· role of context in recognition
· unsupervised and semi-supervised learning from images
A complete list of topics and papers is here.
Students will be responsible for writing short paper reviews, participating in discussions, presenting once or twice throughout the semester, and completing a research-oriented project (done in pairs). Our discussions will center around both the relevant research papers as well as ongoing progress on the class projects.
Courses in computer vision and/or machine learning, probability, linear algebra; ability to understand and do a high-level analysis of conference papers in this area. Please talk to me if you are uncertain if this course will be a good match for your background.
Students are expected to do the assigned reading, participate in class discussions, write one paper review each week, and complete a final project. In addition, everyone will be responsible for giving two presentations: one that involves doing background research on a topic (chosen from the provided list), and one that involves an experimental demo relevant to one of the topics. The two presentations should be on different topics. After each presentation the class will provide feedback for the presenter. Details on each of these elements are provided here.
Grades in the class will be determined as follows:
· 20% Participation (including attendance, in-class discussions, paper reviews, reviews of student papers)
· 35% Presentation and demo
· 45% Final project (including proposal and final paper)
Please read the UTCS code of conduct.
March 14: Spring break, no class
April 18: Project paper rough drafts due
May 2: Final project papers due
There is no required textbook for this course, as we will get most of our content from the papers we read. However, you may find these books useful references:
· Computer vision, Linda G. Shapiro and George C. Stockman.
· Introductory techniques for 3-D computer vision, Emanuele Trucco and Alessandro Verri.
· Multiple view geometry in computer vision, Richard Hartley and Andrew Zisserman.
· OpenCV (open source computer vision library)
· Weka (Java data mining software)
· Netlab (Matlab toolbox for data analysis techniques, written by Ian Nabney and Christopher Bishop)