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
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 |
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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).
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.
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.
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.
·
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.