Instructor: Prof. Harry Wechsler email@example.com
Course Description – Computer Vision (3:3:0). Study of computational models of visual perception and their implementation in computer systems. Topics include early visual processing, edge detection, segmentation, intrinsic images, image modeling, representation of visual knowledge, and image understanding.
Objectives – Course starts with a primer on probability and statistics and another primer on digital image processing (DIP); continues with basics / motivation / image formation process (“vision architectures”) for the purpose of identification / recognition (“what /who”), location (“where”), and behavior (“motion”, “(video) tracking”, and “human activity”). Hands-on experience is expected using MATLAB (and its tool boxes), OpenCV, R, and/or mobileCV. Major applications discussed are those of object representation and recognition, tracking, and biometrics / face & gesture recognition.
(Reference) Textbook: Szelisky, Computer Vision, Springer, 2011.
Day, Time, and Venue: T – Tuesday, 7:20 pm – 10:00 pm, Innovation Hall 206.
Office Hours: Tuesday, 6:00 – 7:00 pm (ENGR - 4448)
First day of classes: Tuesday, January 22
Spring break: no class on Tuesday, March 12
Midterm (“closed books and closed notes”): Tuesday, March 19
Last day of classes: Tuesday, April 30
FINAL Exam – cumulative - (“closed books and closed notes”): Tuesday, May 14, 7:30 pm – 10:15 pm
- 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%
Tentative Course Schedule:
Textbook (Chaps. 1 – 5, 8, 11, 14), Notes / Journal & Conference Papers:
· Overview and Vision Architectures (1/22)
· Primer on Probability / Bayes and Digital Image Processing (1/29)
· Image formation and Digital Forensics (2/5)
· Operators, Filters, and Transforms (2/12)
· Feature Detection (2/19)
· Image Segmentation (2/26)
· Optical Flow and MIDTERM REVIEW (3/5)
· SPRING BREAK (3/12)
· MIDTERM (3/19)
· Correspondence and Stereo (3/26)
· Tracking (4/2)
· Object Recognition (4/9)
· Biometrics (4/16)
· Term (Team) Project Presentation (4/23)
· FINAL (cumulative) REVIEW (4/30)
You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at http://academicintegrity.gmu.edu/honorcode/.
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies