Chenliang Xu
Meet Time/Location: T/R 09:40-10:55 in Gavett 202
Instructor: Prof. Chenliang Xu
Instructor's Office Hours: Tue 2:00-4:00 pm in Wegmans 3005
TAs and Office Hours:
Information Flow: We will use Blackboard to make announcements, host lecture notes/assignments, and submit/grade student work. The course ID is CSC249.1.SPRING2024ASE. We will use Blackboard's Discussion Board for class-related discussion and communication, so that you can get help fast from the teaching team and your fellow classmates. Don't hensitate to email me and the TAs if it is a personal matter. Finally, this course website containing the course schedule serves as a syllabus.
Credit Hours: This is a four-credit-hour course. The credit hours are met by weekly lectures (150 minutes), office hours, homework assignments, and the final course project.
Fundamentals of computer vision, including image formation, elements of human vision, low-level image processing, and pattern recognition techniques. Advanced topics include modern visual features, graphical models, model-based and data-driven approaches, and contextual inference, as well as illustrative examples of successes and challenges in applications. CSC 449, a graduate-level course, requires additional readings and assignments.
Rough Topic Outline:Course Objective: An introduction to computer vision. The course is designed as an upper-level elective for Computer Science undergraduate students and an AI-area breath course for graduate students. The students will develop a strong understanding of formulating and solving problems using computer vision.
Prerequisites: MATH 161 and CSC 242; MATH 165 strongly recommended; in addition, you should be familiar with Python, which is used for all programming assignments.
CS Instructional Network: The course programming assignments require you to have an account in the CS instructional network. You should test your code in the CS instructional network before submission as this is the environment we use to grade assignments. Follow this link to request an account if you don't have one.
This is a tentative course schedule and is subject to changes over the term.
| Dates | Tuesday (T) | Thursday (R) | Notes | Read |
|---|---|---|---|---|
| 1/16,18 | No Class: Before Term | Introduction | ||
| 1/23,25 | Camera Models I | Camera Models II | 2.1, 3.2-3.3.1 | |
| 1/30,2/1 | Linear Filtering | Programming Tutorial (by Andy) | PS-1 Out | 7.1, 7.2 |
| 2/6,8 | Edges | Corners | 7.4, 8.1.4 | |
| 2/13,15 | SIFT | Feature Matching and Image Alignment | 8.1 | |
| 2/20,22 | Proj. Teaming & Proposal (by Susan) | Fitting I (Zoom) | PS-1 Due | 9.1, 9.3, 11.1-.3 |
| 2/27,29 | Fitting II | Dense Motion Estimate | PS-2 Out | 7.5, 5.3 |
| 3/5,7 | Visual Odometry | Segmentation | 5.4, 5.5 | |
| 3/12,14 | Spring Break | Spring Break | ||
| 3/19,21 | Learning Features I | Guest Lecture (by Prof. Jason Corso in CSB 209) |
PS-2 Due | |
| 3/26,28 | Learning Features II | Learning Features III | ||
| 4/2,4 | Object Recognition | Face Recognition | ||
| 4/9,11 | Generative Image Models | Vision and Language | ||
| 4/16,18 | Exam Review (by Luchuan) | Exam | ||
| 4/23,25 | Project Presentation I (Zoom) | Project Presentation II | ||
| 4/30,5/2 | Project Presentation III | No Class: Term Finished | Report Due |
Late Work Policy: Deduct 1% total score of the late assignment per hour passing the deadline. E.g., you submit PS-1 12 hours passing the deadline, then the max score you will get is 88%. We will not accept your submission after 72 hours over the deadline. All assignments should be submitted on Blackboard.
Regrading: Any questions about the grading of a piece of work must be raised within one week of the date that the work was returned. In other words, if you do not pick up your work in a timely fashion, you may forfeit your right to question the grading of your work.
We will use the following textbook by Richard Szeliski. Please note that we will use the 2nd edition, which was recently updated with many new contents.
Academic Honesty: All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available here. You are expected to read, understand, and follow the policy. Additionally:
Posting homework and project solutions to public repositories on sites like GitHub is a violation of the College’s Academic Honesty Policy, Section V.B.2 “Giving Unauthorized Aid.”
Disabilities: Please see me about your required accommodations as early as possible in the term. The University of Rochester respects and welcomes students of all backgrounds and abilities. In the event you encounter any barrier(s) to full participation in this course due to the impact of a disability, please contact the Office of Disability Resources. The access coordinators in the Office of Disability Resources can meet with you to discuss the barriers you are experiencing and explain the eligibility process for establishing academic accommodations. You can reach the Office of Disability Resources at: disability@rochester.edu; (585) 276-5075; Taylor Hall; www.rochester.edu/college/disability.