Instructor: Prof. Harry Wechsler email@example.com
Course Description – Computer Vision (3:3:0). Prerequisite: Grade of C or better in MATH 203, STAT 344 and CS 310. Basic principles of visual perception and their implementation on computer systems. Topics include early visual processing, edge detection, segmentation, intrinsic images, image modeling, representation of visual knowledge, and image understanding. Students complete projects involving real images
Objectives – Starting with a primer on probability and statistics and another primer on digital image processing; continuing with basics on computer vision (“how”) for the purpose of location (“where”), behavior (“motion” and “human activity”) and identification (“how”). All this includes motivation and utility for what is learned and hands-on experience using MATLAB (and its tool boxes) and OpenCV. Major application discussed is that of biometrics for the purpose of face recognition.
knowledge of image formation process
- Basic knowledge of image processing techniques for color and gray level images: edge detection, corner detection, segmentation
- Basics of video processing, motion computation and 3D vision and geometry
- Ability to implement basic vision algorithms in MATLAB and use OpenCV (open source computer vision library)
- Ability to apply the appropriate technique to a problem, write a project report and present the results in class.
Time, Day, and Venue: R – Thursday, 4:30 pm – 7: 10 pm, Innovation Hall 204
Office Hours: Thursday, 3:15 – 4:15 pm (ENGR - 4448)
First day of classes: Thursday, January 26
Spring break: no class on Thursday, March 15
Midterm (“closed books and closed notes”): Thursday, March 22
Last day of classes: Thursday, May 3
Final Exam: Thursday, May 10, 4:30 pm – 7:15 pm
Grade Composition: 100%
- Homework: 25%
Homework and term project (see below) require using MATLAB and/or OpenCV. You can buy a student version for MATLAB in Johnson center or use it remotely from ITE labs. OpenCV is a C/C++ open source computer vision library. You can also use image processing software for mobile applications, Android and iPhone.
- Midterm: 25%
- Term (team) Project: 25%
- FINAL: 25%
Textbook: Szelisky, Computer Vision, Springer, 2011
- Textbook (Tentative: Chaps. 1 – 5, 8, 11, 14):
· Over view and Vision Architectures
· Primer on Probability / Bayes and Digital Image Processing (using MATLAB)
· Image formation
· Operators, Filters, and Transforms
· Feature Detection
· Optical Flow
· Correspondence and Stereo
· Object Recognition and Biometrics
- MATLAB and OpenCV
- Class Notes and Papers
Term (Team) Project ~ Presentation and Final Report ~ Wednesday, May 2
GMU is an Honor Code university; please see the University Catalog for a full description of the code and the honor committee process. The principle of academic integrity is taken very seriously and violations are treated gravely. What does academic integrity mean in this course? Essentially this: when you are responsible for a task, you will perform that task. When you rely on someone else’s work in an aspect of the performance of that task, you will give full credit in the proper, accepted form. Another aspect of academic integrity is the free play of ideas. Vigorous discussion and debate are encouraged in this course, with the firm expectation that all aspects of the class will be conducted with civility and respect for differing ideas, perspectives, and traditions. When in doubt (of any kind) please ask for guidance and clarification.
GMU EMAIL ACCOUNTS
Students must use their Mason email accounts—either the existing “MEMO” system or a new “MASONLIVE” account to receive important University information, including messages related to this class. See http://masonlive.gmu.edu for more information. NOTE: Weekly email messages
are sent to the class including among others reading assignments, homework, and journal / conference papers.
OFFICE OF DISABILITY SERVICES
If you are a student with a disability and you need academic accommodations, please see me and contact the Office of Disability Services (ODS) at 993-2474. All academic accommodations must be arranged through the ODS. http://ods.gmu.edu