CSE527: Introduction to Computer Vision

http://www.cs.stonybrook.edu/~cse527

Instructor: Prof. Dimitris Samaras

Spring 2024: Mon-Wed 7:00 – 8:20 in New Computer Science 120 Or Zoom: https://stonybrook.zoom.us/j/95588187655?pwd=N0lLLzd0VlZaM3ZLZ2JSZ1ptbStIUT09

Course Syllabus

The aims of this course are to provide an understanding of the fundamentals of Computer Vision and to give a glimpse in the state-of-the-art, at a moment when the field is achieving "critical mass" and has significant commercial applications. Apart from basic theory we will look at applications of Computer Vision in Robotics, Graphics and Medicine. Topics in this course:

 

1.     Image Formation

o  Geometric and photometric basis of imaging

o  Camera geometry

2.     Image processing

o  Pixel-level operations

o  Filtering

3.     Model Fitting

o  Under- and over-fitting

o  Robust fitting

4.     Machine learning

o  Basic concepts in machine learning

o  Deep learning

5.     Visual recognition

o  Object recognition

o  Object detection

o  Semantic segmentation

6.     Feature matching

o  Local features

o  Hough transform and RANSAC

 

7.      Motion

o   Translational alignment

o   Optical flow

o   Object tracking

8.      Structure from motion

o   Two- and Multi-frame SfM

o   SLAM

9.      Depth estimation

o   Epipolar geometry

o   Stereo 3D reconstruction

10.   Augmented reality

o   Illumination

o   Neural Rendering

11.   Deep Generative Models

o   Autoencoders

o   Generative Adversarial Networks

o   Diffusion Models

 

Intended Audience:

This course is intended for graduate students with interests in all areas of  Visual Computing and Machine Learning, such as Computer Vision, Computer Graphics, Visualization, Biomedical Imaging, Robotics, Virtual Reality, Computational Geometry, Optimization, Deep Learning, HCI. Prerequisites include a foundation in Linear Algebra and Calculus, and the ability to program. We will be programming in Python (OpenCV, NumPy, SciKit).

 

Grading:

There will be five homeworks, three quizzes, a midterm and a final. Homeworks will be 40%, quizzes 10% total, the midterm 25%, and the final 25%. Weights are approximate and subject to change. You are expected to do homeworks by yourselves. Even if you discuss them with your classmates, you should turn in your own code and write-up.  Do not share your code! There will be 4 free late dates for the semester. After that there will be 10% penalty per day.

 

You can do a project instead of the final exam. Projects will be done in up-to 2 people teams, and will require a significant programming and documentation effort. This will probably be much more work than taking the final. Two people projects will be scaled accordingly.

 

Quiz 1: Feb 5th 2024 (tentative)

Quiz 2: Feb 21st 2024 (tentative)

Quiz 3: April Apr 10th (tentative

Midterm date:  Mar 20th 2024,

Final date: May 13th  2024. Projects due May 15th 2024.

You can have one sheet of paper with notes in the exams.

 

Textbook:

Computer Vision: Algorithms and Applications by Richard Szeliski (2nd  ed. 2022) Main text, available online.

Also: Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2012)

Deep Learning, Goodfellow and Yoshua Bengio, Aaron Courville, 2016, MIT press.

Readings from these books and notes for all topics will be posted on Brightspace.

 

Student Accessibility Support Center Statement:

If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, Stony Brook Union Suite 107, (631) 632-6748, or at sasc@stonybrook.edu. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential.

 

Academic misconduct policy:

Don't cheat. Cheating on anything will be dealt with as academic misconduct and handled accordingly. I will not spend a lot of time trying to decide if you actually cheated. If I think cheating might have occurred, then evidence will be forwarded to the University's Academic Judiciary and they will decide. If cheating has occurred, an F grade will be awarded. Discussion of assignments is acceptable, but you must do your own work. Near duplicate assignments will be considered cheating unless the assignment was restrictive enough to justify such similarities in independent work. Just think of it that way: Cheating impedes learning and having fun. The labs are meant to give you an opportunity to really understand the class material. If you don't do the lab yourself, you are likely to fail the exams. Please also note that opportunity makes thieves: It is your responsibility to protect your work and to ensure that it is not turned in by anyone else. No excuses! The University has a relevant policy:

Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Professions, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at: 

http://www.stonybrook.edu/commcms/academic_integrity/index.html

 

Critical Incident Management:

Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of University Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook.

 

Contact info:

·           Dimitris Samaras, Email: samaras@cs.stonybrook.edu  

·           Office Hours: Wed., 3-5 pm, or by appointment, in room NCS 263

·           TAs: Jason Qin, jaqin@cs.stonybrook.edu, Rineeth Modhugu, rmodhugu@cs.stonybrook.edu

·           Office Hours: Friday 4-6pm in room CS 2126.