CS 884
Advanced Topics in Computer Vision and Robotics

Time/Location: Tuesday 4:30-7:10pm,  Innovation Hall 208
Instructor: Jana Kosecka
Office hours:  2-3pm Wednesday
Contact: Office 4444 Engineering Build., e-mail: kosecka@gmu.edu, 3-1876
Course web page: http://www.cs.gmu.edu/~kosecka/cs884/

This course will cover computer vision techniques for object and category recognition, recognition of human activities from video streams and ego-centric vision. Recognition of objects instances (my cup, cereal box) or generic categories (any cup, bottle) is an essential capability for a variety of robotics and multimedia applications. This course will provide a comprehensive survey of the existing methods and discuss the performance of reviewed methods on several benchmark datasets. We will also consider the techniques needed for real-time interactive applications on robots or mobile devices, e.g. domesticservice robots or mobile phones that can retrieve information about objects in the environment based on visual observation.

The class will consist of lectures by the instructor and weekly reading of the assigned papers.The students will be responsible for presentation of the selected paper and implementation of some methods as part of homeworks. The papers will be selected from the recent proceedings of Computer Vision and Robotics Conferences and journals. The grade will be based on paper presentations, homeworks and final presentation of the project.

Prerequisites:

Students should have one (ideally two) of the following as prerequisites. CS 682 Computer Vision, CS 685 Autonomous Robotics, CS 687 Advanced Artificial Intelligence, 688 Patter Recognition or Advanced Data Mining Class or ask for a permission of an instructor.

Students taking the class should be comfortable some image processing in Matlab (introduction to feature computation will be provided) and basic machine learning techniques; such as Support Vector Machines, Decision Trees, Boosting, Hidden Markov Models, LDA, Logistic Regression, Gaussian Mixture Models etc.

Preliminary Readings http://www.cs.gmu.edu/~kosecka/cs884/

Grading

Homeworks, Presentations 60% and Project 40%
The homeworks should be handed in on time.  Late submissions are accepted but will incur late submission penalty.

Academic Integrity:

The integrity of the University community is affected by the individual choices made by each of us. GMU has an Honor Code with clear guidelines regarding academic integrity. Three fundamental and rather simple principles to follow at all times are that: (1) all work submitted be your own; (2) when using the work or ideas of others, including fellow students, give full credit through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment, ask for clarification. No grade is important enough to justify academic misconduct. Plagiarism means using the exact words, opinions, or factual information from another person without giving the person credit. Writers give credit through accepted documentation styles, such as parenthetical citation, footnotes, or endnotes. Paraphrased material must also be cited, using MLA or APA format. A simple listing of books or articles is not sufficient. Plagiarism is the equivalent of intellectual robbery and cannot be tolerated in the academic setting. If you have any doubts about what constitutes plagiarism, please see me.

CS department Honor Code can be found here.