CSC I6716 Computer Vision- Spring 2013
Instructor: Professor Zhigang Zhu
Teaching Assistant: Mr. Wai L. Khoo
Section: 2TU Code: 3073 Credits: 3.0
Class Meet Time: Tuesdays 7:30 - 10:00 PM , Room: NAC 7/227
Office Hours: Tuesdays 4:00 - 5:00 pm , then 600 - 7:00 pm, Room: NAC 8/211
City College of New York
Jan 29 (Tuesday), 2013. First class meet of our course.
Feb 04, 2013. Slides for Image Formation
(including a slide on installing Matlab in your machine)
Feb 04, 2013, Assignment 1 online (including a quick matlab tutorial)
Feb 12, 2013, Assignment 2
online. due March 05 in class.
Feb 26, 2013, Assignment 3 online. due March 19 in class.
March 05, 2013. Grading for Assignment 1.
March 18, 2013. Grading for Assignments 1-2.
March 18, 2013. Some ideas for your course projects (please use the same user and password as for the lecture notes to access). We will discuss this in class on March 19.
March 18, 2013, Assignment 4 online. due April 30 in class. Deadline for Assignment 3 could be extended to April 09 so you could use your spring break to work on it.
April 22, 2013. Grading
for Assignments 1-3.
May 03, 2013. Project
Presentation Schedule and Requirements. All
presentations are scheduled on May 14, each student 10 minute.
Please bring your project report to class on May 14 for the
final presentation - each team has one report. Please come to
class on May 07 as well for discussions (account 15% of your
report) - please bring your machines if you want to show me
something for feedback.
May 06, 2013. Grading
for Assignments 1-4 and Exam.
May 24, 2013. Final Grading.
Computer vision has a rich history of fundamental work on stereo and visual motion, which has dealt with the problems of 3D reconstruction from multiple images, and structure from motion from video sequences. Recently, in addition to these traditional problems, the stereo and motion information presented in multiple images or a video sequence is also being used to solve several other interesting problems, for example, large-scale scene modeling, video mosaicing, video segmentation, video compression, video manipulation and video surveillance. This is sometimes summarized as video computing. Computer vision is playing an important and somewhat different role in solving these problems in video computing than the original image analysis approach in the early days of vision research.The course "Computer Vision" will include advanced topics in video computing as well as fundamentals in stereo and motion.
(Spring 2013 academic calendar)
Part I. Computer Vision Basics
I-1. Introduction: What, Why and How (pptx slides) [printable PDF] -01/29
I-2. Image Formation: Digital Image Basics (pptx slides) [printable PDF] (Assignment 1)-02/05
I-3. Image Enhancement (slides) (Assignment 2) (Updated lecture notes on feature extraction:I-3 and I-4) -02/14
I-4. Edge Detection: (slides) - 02/19
Part II. 3D Computer Vision
II-1. Camera Models (slides) (lecture
notes in PDF) (Assignment 3)
II-2. Camera Calibration (slides) (lecture
notes in PDF ) - 03/05,
(Problem Definition: the Tools You Must Know),
(Direct Approach: Divide and Conquer),
(Projective Matrix Approach: All in One )
II-3. Stereo Vision (slides) (lecture
notes in PDF) (Assignment 4)
(Problem Definition & Epipolar Geometry) , Project Discussions
(Correspondence Problem & Reconstruction Problem)
II-4. Visual Motion - (slides) (lecture
notes) - 04/16, 04/23
(The Motion Field of Rigid Motion)
(Optical Flow Approach &
& Exam Review
Part III. Exam, Projects and Project Presentations
III-1. Exam -04/30
III-2. Exam and Homework Discussions; Project Discussions 05/07
III-3. Student Project
Presentations - 05/14
In the form of Lecture Notes and Slides; will be provided by the instructor
Online References and additional readings when necessary.
The course will accommodate both graduate and senior undergraduate students with background in computer science, electrical and computer engineering, or applied mathematics. Students who take the course for credits will be required to finish 4 assignments (40%), one midterm exam (40%), and one programming project (20%, including submit a report and give a small presentation to the class at the end of the semester). The topics of the projects will be given in the middle of the semester and will be related to the material presented in the lectures.
Students are required to have a good preparation in both mathematics (linear algebra/numerical analysis) and advanced programming.
Copyright @ Zhigang Zhu , Spring 2013