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Class time: Tuesday and Thursday, 2:20 pm - 3:40 pm
Classroom: Scott Lab E024
Course website: https://sites.google.com/view/osu-cse-5524-sp25-chao
Instructor: Prof. Wei-Lun (Harry) Chao
Email: chao.209@osu.edu
Office hours: 11 am - noon Tuesday and 9 - 10 am Thursday (DL 587)
Email: karimimonsefi.1@osu.edu
Office hours: 9 - 10 am Monday and 10 - 11 am Wednesday (BE406)
Course information
Course information
Syllabus: Link (Pay attention to the academic misconduct statement)
Course Description: Computer vision algorithms for use in human-computer interactive systems; image formation, image features, segmentation, shape analysis, object tracking, motion calculation, and applications.
Course Goals / Objectives:
Master fundamental and recent computer vision algorithms
Be competent with computer vision application design and evaluation
Be familiar with the Python/PyTorch programming environment
Be exposed to original research and applications in computer vision
Course Credits: 3 units
Pre-requisites:
Required background:
§ Data structures and algorithms: 2331
§ Statistics and probability: 5522, Stat 3460, or 3470
Suggested background:
§ Linear algebra: Math 2568, 2174, 4568, or 5520H
§ Artificial intelligence: 3521, 5521, or 5243
Students are expected to have a decent degree of mathematical sophistication, better familiar with linear algebra, multivariate calculus, probability, and statistics. Students are also expected to know programming, algorithm design, and data structures.
Programming in Python 3 is required. Programming in PyTorch and using Hugging Face might be needed.
Review materials can be found here: linear algebra, probability, Python-1, Python-2, Python-3
Also, check HERE for a set of slide decks for linear algebra
Required Textbook:
Antonio Torralba, Phillip Isola, and William T. Freeman, Foundations of Computer Vision. MIT Press, 2024. https://mitpress.mit.edu/9780262048972/foundations-of-computer-vision/ (Purchasable on MIT Press Bookstore or Amazon)
Suggested References:
Richard Szeliski, Computer Vision: Algorithms and Applications (second edition). Springer, 2022.
David Foster, Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play (second edition). O'REILLY, 2023. https://library.ohio-state.edu/record=b10787441
Other Good References:
Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, Dive into Deep Learning. 2021. https://d2l.ai/index.html
Simon J. D. Prince, Understanding Deep Learning. The MIT Press, 2023. https://udlbook.github.io/udlbook/
Christopher M. Bishop and Hugh Bishop, Deep Learning: Foundations and Concepts. Springer, 2024.
Other Good CV Courses:
Stanford CV: http://vision.stanford.edu/teaching/cs131_fall2223/ and https://cs231n.stanford.edu/
MIT CV: http://6.869.csail.mit.edu/sp22/schedule.html and https://advances-in-vision.github.io/schedule.html
Brown CV: https://browncsci1430.github.io/
Wisconsin-Madison CV: https://sites.google.com/view/cs639spring2023dlcv
Michigan CV: https://web.eecs.umich.edu/~justincj/teaching/eecs442/WI2021/ and https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/
Cornell CV: https://www.cs.cornell.edu/courses/cs4670/2021sp/ and https://www.cs.cornell.edu/courses/cs6670/2023fa/
PyTorch:
Useful Reference:
Kaare Brandt Petersen and Michael Syskind Pedersen, The Matrix Cookbook
Grading (tentative):
Quizzes (including linear algebra): 10%
Homework: 40%
Midterm exam (3/4/2025, in class): 20%
Final project (presentation 4/22 & 4/23, 2:00 pm - 3:45 pm): 30%
Please reserve 4/22, 2:20 pm - 3:40 pm (Tuesday) for additional project presentation time.
Homework:
There will be around 6 homework assignments.
Each assignment may include a problem set and a programming set.
Programming in Python 3 is required.
Carmen (and other platforms like GitHub) will be used for submission.
For the problem set and the report of the programming set, we will only allow PDF submission.
You must strictly follow the homework and submission instructions.
Quizzes:
We will use Carmen's quizzes if there are any.
Midterm exam & final exam:
The midterm and final exams are in person.
The final exam is, by default, cumulative.
Exam materials/questions may come from the reading listed in the schedule.
The final exam "is" replaced by a final project.
Final project:
Strongly suggest that you get familiar with PyTorch, GitHub, and Hugging Face
TBA
Announcements, communications, and discussions:
We will make normal announcements using the Carmen Canvas. Announcements of urgent matters will be mailed to your name.#@osu.edu address. If you do not regularly read that account, make sure you forward it to somewhere that does.
We will use Piazza for discussions. If you have questions about the course materials or policy, please post them on Piazza. The TA and I will also monitor these discussions and answer as appropriate, but students should be active and feel free to use the forums to have group discussions as well.
Please only use email to contact the instructor or the TA for urgent or personal issues. Any e-mails sent to the instructor or TA should include the tag "[OSU-CSE-5524]" in the subject line. (This ensures we can filter and prioritize your messages.) We reserve the right to forward any questions (and their answers) to the entire class if they should prove relevant. Please indicate if you wish to be anonymized (i.e. have your name removed) in this case.
Policy (see the syllabus for details)
Policy (see the syllabus for details)
Homework:
There are NO late days for homework assignments.
Homework should be neat and professional and follow the required format. In particular, homework on torn sheets, scrap paper, or not well-scanned into a single file will not be accepted.
Homework is to be done individually. Of course, the discussion between students is allowed and encouraged, but the actual homework should be completed separately. You have to list with whom you discussed.
Questions about homework or exams should be made in a timely fashion. Any complaints about homework grading must be made within 1-week of when the item is returned or before 4/23/2025 (whichever comes first). Do not wait until the end of the semester!
Exam:
Excuses from scheduled exams can be accepted only in case of personal sickness requiring medical care or severe accidents in the immediate family (documentation required).
Weekly schedule (tentative)
Weekly schedule (tentative)
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