Human & Computer Vision with Advanced Topics – Spring 2023
- Instructor: Professor Zhigang Zhu
- The CUNY Graduate Center and City College
- Course Code: CSC 83020 – 01 (55719) Human & Computer Vision with Advanced Topics
- Time: Wednesday 9:30 – 11:30 am; Location (Hybrid): (1) In-person at The CUNY Graduate Center Rm 3207 (noted in the syllabus), (2) Online [Class Zoom Link]
- Office Hours: Thursday 2:00 pm – 4:00 pm [Office Hours Zoom Link].
- TA: Bilal Abdulrahman <email@example.com>
Course Update Information
- 01/25/2023: First Day of This Class. Most of the class meets will be in-person, but we will have Zoom options for all classes for those who cannot make the in-person classes. But a few classes would be fully online. Details please see the tentative schedule (up to changes).
- 02/07/2023. Overall Grading Landscape and Grading So Far.
- 03/02/2023. Grading for Assignment 1, etc..
- 03/23/2023. Grading for Assignments 1-2, etc..
- 03/31/2023. Grading for Assignments 1-2, Exam, and etc.. We will discuss the Exam on 04/19, right after the Spring Recess. Enjoy your spring break!
- 04/05/2023. Grading for Assignments 1-2, Exam, Project Proposal and etc.. Please make sure you develop your project presentation and report following the guideline!
- 04/25/2023. Grading for Assignments 1-3, Exam, Project Proposal and etc. (with updates).
- 04/26/2023. Grading with attendance update for today’s class.
- 05/10/2023. Grading for project presentations.
- 05/18/2023. Final Grading will be submitted on Wednesday May 24 , 2023. Have a great summer!
This course will cover the fundamental work on color, shapes, stereo and visual motion, which has dealt with the problems of image understanding, 3D reconstruction from multiple images, and structure from motion with video sequences. In addition to these traditional problems, we will also showcase a recent example in successfully using image processing, computer vision and machine learning techniques for transforming large transportation centers into Smart and Accessible Transportation Hubs (SAT-Hubs) for serving people with disabilities. Moreover, the best successful vision system that computer vision researchers can learn from is the human vision system. Therefore this course will briefly discuss human vision science and explore how the brain sees the world too.
Course Syllabus and Tentative Schedule (mm/dd)
Introduction and Course Organization (slides) – 01/25
Part I. Basics of Human and Computer Vision
I-1. Human Eyes (slides) – 01/25 (Assignment 1 on I-1, I-3 and I-4)
I-2. Visual Brain (slides) – 02/01
I-3. Depth (slides) and Color (slides)- 02/08
I-4. Features (slides) (lecture notes) – 02/15
Part II. 3D Computer Vision
II-1. Camera Models (slides) (lecture notes) (recorded video of the class) – 02/22 (Assignment 2 on II-1 and II-2)
II-2. Camera Calibration (slides) (lecture notes) (recorded video of the class) – 03/01
II-3. Stereo Vision (slides) (lecture notes) (recorded video of the class-the first part before break is good, but no audio in the second part) [Okay audio added for the second part!] – 03/08 (in-person) & 03/15 (No Class meet) [The first recorded video for stereo-part 2] (Assignment 3 on II-3 and II-4) (Course Project Ideas)
II-4. Visual Motion (slides) (lecture notes) – 03/15 (No Class meet) [The second recorded video for motion part-1] & 03/22 (in-person) [recorded video of visual motion part-2] (Exam Review) (Project Topics Due 04/04 before Spring Recess)
Mid-Term Exam – 03/29 [Online over Zoom 9:30 AM – 11:00 AM due to FIRE DRILL – WEDNESDAY, March 29, 2023, 10:00 AM] (Spring Recess 04/05-04/13)
Part III. Advanced Topics
III-1. Vision in Space and Time (Mosaics and Panoramas [recorded video]) – 04/19 (with Exam Discussion)
III-2. Vision and Assistive Technology (MAP4VIP (slides), SAT-Hub and iASSIST [recorded video]) – 04/26
III-3. Understanding People (Social and Crowd)[Recorded video]) – 05/03 [Via Zoom]
IV-4. Understanding Places (Context and Self-Supervision)[Recorded video]) – 05/03 [Via Zoom]
All Student Project Presentation – 05/10 (via Zoom, Last class on Wednesday); Project Reports by the end of 05/15 (Monday).
Textbook and References
- Computer Vision, In the form of Lecture Notes and Slides; will be provided by the instructor
- Vision and Brain – How We Perceive the World, By James V. Stone, The MIT Press. Paperback | $30.00 | ISBN: 9780262517737 | 264 pp. | 6 x 9 in | 25 color illus., 132 b&w illus.| September 2012 (For students with little experience in vision and neuroscience to know human vision, brain and computational neuroscience)
Online References and additional readings when necessary.
Grading and Prerequisites
The course will accommodate both PhD students in Computer Science and master level graduate students in Data Science and Cognitive Neuroscience at the CUNY Graduate Center. Students who take the course for credits will be required to finish 3 assignments (15% for each), one midterm exam (30%), and one programming project (25%, including submit a report (10%) and give a presentation (15%) 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.
For the assignments and the projects, students may discuss ideas together. But since each student get credits for his or her submissions, all actual program code and written answers must be done individually by each student, and must not be shared. The midterm exam will be a close-book exam. You will need to clear state that you will neither give nor receive unauthorized assistance on this exam.
We fully support CUNY’s policy on Academic Honesty, which states, in part:
Academic dishonesty is unacceptable and will not be tolerated. Cheating, forgery, plagiarism, and collusion in dishonest acts undermine the CUNY’s educational mission and the students’ personal and intellectual growth. Students are expected to bear individual responsibility for their work, to learn the rules and definitions that underlie the practice of academic integrity, and to uphold its ideals. Ignorance of the rules is not an acceptable excuse for disobeying them. Any student who attempts to compromise or devalue the academic process will be sanctioned.
Academic sanctions in this class will range from an F on an assignment to an F in this course.
Students are required to have a good preparation in both mathematics (linear algebra/numerical analysis) and advanced programming.