CS 482
Computer Vision

Time/Location: Tuesday/Thursday 10:30-11:45,   Music/Theater Building 1005
Instructor: Dr. Jana Kosecka
Office hours: by appointment or Thursday 2-3pm
TA: Yong Yang yyang29@gmu.edu
TA office hours: Wed 3-5pm
Office: 4444, Research II
email: kosecka@gmu.edu
Course website http://cs.gmu.edu/~kosecka/cs482/
Course communication Piazza

This course will cover essentials of Computer Vision, a discipline that strives to develop techniques to help computers "see" and understand images. The course is of interest to anyone seeking to process images and acquire a general background in problems related to real-world perception, object and scene recognition and 3D reconstruction. The geometric aspects of the course will focus on extracting 3D metric information from 2D images. The second theme covers methods for extraction of semantic information. This will entail problems of image classification, object detection (e.g. how to detect people, cars or other object of interest in images), activity recognition. Aplications to 3D modelling, video analysis, video surveillance, image based retrieval, object detection and recognition and vision based control will be discussed.  


Grading Homeworks (about every 2 weeks) 50% Exam: 30% Final project: 20%
Prerequisites linear algebra, calculus, probability and statistins
Lecture Materials Lecture slides, lecture notes provided by instructor

Recommended Textbooks

[1] Invitation to 3D Vision: From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S. Sastry web site
[2] Computer Vision: Algorithms and Applications. R. Szeliski, 2010, Springer online version of the book
Additional Resources
[3] Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003
[4] Image Processing, Analysis, and Machine Vision. Sonka, Hlavac, and Boyle. Thomson.
[5] Computer Vision. Ballard and Brown web site

Required Software

Python, OpenCV

Course Outcomes

Basic 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
Basics of image classification, object detection and recognition video processing
Ability to implement basic vision algorithms in Python/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.

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.