Instructor: Prof. Dimitris Samaras
Fall 2014: Tue-Thu 4-5:20 in 2311 Computer Science
The aims of this course are to provide an understanding of the fundamentals of Computer Vision and to give a glimpse in the state-of-the-art, at a moment when the field is achieving "critical mass" and has started having significant commercial applications. Topics this course will cover include:
1. Image Formation
Robustness, Maximum Likelihood
Scale, Orientation, Fourier Transforms
Shading, Shadows, Interreflections,
Reflectance properties of materials
7. Camera Geometry
Multiple View Geometry -
3D Shape from X
11. Segmentation & Grouping
Grouping, Probabilistic Fitting,
Nearest Neighbors, EM,
MRFs and Optimization
Tracking, Kalman Filtering
Deep Learning Classifiers
This course is intended for graduate students with interests in all areas of Visual Computing, such as Computer Vision, Computer Graphics, Visualization, Biomedical Imaging, Robotics, Virtual Reality, Computational Geometry. Prerequisites include a foundation in Linear Algebra and Calculus, and the ability to program, preferably in C/C++._
There will be homeworks, a final project, and two midterms. Homeworks will be 35%, the project 30%, and the exams 35%. Weights are approximate and subject to change. You are expected to do homeworks (4 or 5) by yourselves. Even if you discuss them with your classmates, you should turn in your own code and write-up. Final projects can be done by one or two people. Two people projects will be scaled accordingly.
Midterm 1 date: Oct 14th 2014
Midterm 2 date: Dec 4th, 2014
You can have one sheet of paper with notes in the midterms.
Computer Vision: A Modern Approach, Forsyth and Ponce, 2nd ed. Prentice Hall 2011.
(Optional) Computer Vision: Algorithms and Applications by Richard Szeliski, Microsoft Research: draft at http://szeliski.org/Book/
(Optional) Computer Vision: Models, Learning, and Inference by S. Prince, Cambridge University Press, 2012
Introductory Techniques for 3-D Computer Vision by E. Trucco
and A. Verri,
Prentice Hall, Upper Saddle River, N.J., 1998
Class notes and a collection of additional readings from journals and conference proceedings will be available through Blackboard.
Don't cheat. Cheating on anything will be dealt with as academic misconduct and handled accordingly. I won't spend a lot of time trying to decide if you actually cheated. If I think cheating might have occurred, then evidence will be forwarded to the University's Academic Misconduct Committee and they will decide. If cheating has occured, an F grade will be awarded. Discussion of assignments is acceptable, but you must do your own work. Near duplicate assignments will be considered cheating unless the assignment was restrictive enough to justify such similarities in independent work. Just think of it that way: Cheating impedes learning and having fun. The labs are meant to give you an opportunity to really understand the class material. If you don't do the lab yourself, you are likely to fail the exams. Please also note that opportunity makes thieves: It is your responsibility to protect your work and to ensure that it is not turned in by anyone else. No excuses!
If you have a physical, psychological, medical or learning disability that may impact on your ability to carry out assigned course work, I would urge that you contact the staff in the Disabled Student Services office (DSS), Room 133 Humanities, 632-6748/TDD. DSS will review your concerns and determine, with you, what accommodations are necessary and appropriate. All information and documentation of disability is confidential.
D. Samaras, Tel. 631-632-8464
Office Hours: Tue., 2 pm to 3.30pm Wed 2:30pm to 4pm, or by appointment
Computer Science room 2429