All zoom meetings are password-protected. Please find the information in Blackboard.
Meet Time/Location: T/R 12:30-13:45 on Zoom
TAs and Office Hours:
Information Flow: We will use Blackboard to make announcements, posting lecture notes/videos and assignments, and submitting student work. The course ID is CSC249.1.SPRING2021ASE. This course website contains an outline of the course schedule and also serves for the purpose of 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, typically requires additional
readings and assignments. Based on past classes, CSC 249, an
undergraduate-level course, has the same requirements with a
5% bonus for completing the same requirements as 449.
The course is designed as an upper-level elective for Computer Science undergraduate students and an AI-area breath course for graduate students. Students will develop a strong understanding of formulating and solving problems in computer vision.
Basic knowledge of probability, linear algebra (MTH 165 Strongly Recommended); data structures, algorithms; programming experience. For assignments and projects, the most common programming language is Python or Matlab.
You need a computer and a reliable network to access online lectures and course materials. Besides, many of the course assignments require you to have access to a powerful GPU system. This can usually be resolved by:
By taking this course, you confirm that you have met the system requirements.
Machine/Computer Vision is under rapid development over the past few years. There is not a perfect textbook that covers everything needed for an introductory computer vision course in today's context. However, we will use the following textbook, which is widely used in many other computer vision courses.
This is a tentative schedule of the course and is
subject to modifications over the term.
Most lectures will be given in-person and streamed simultaneously via Zoom. Please note all classes will be recorded. The recordings along with any other course materials will be posted on Blackboard for asynchronous viewing by students. Please note that the instructor owns the copyright of all recorded lectures and course materials, You should not post them on the Internet or sharing with a friend without my consent.
|Dates||Tuesday (T)||Thursday (R)||Chapter||Project|
||Image Formation / Camera Models||1,2
||Low-level Image Processing
||Review of Probability
|2/23,25||Pattern Recognition Techniques
||CNN and Feature Visualization||notes
|3/2,4||Modern Visual Features
||Modern Visual Features
|3/9,11||Fitting (Hough Transform)
||Image Registration||Face and People Recognition I
||Face and People Recognition II
|3/30,4/1||Deep Learning and CNN I||Deep Learning and CNN II
|4/6,8||Special Lecture: Vision + Language||Special Lecture: Vision + Language||notes
||Video Analysis I||7,8,11,notes|
||Video Analysis II
||GAN and Image Synthesis||18,notes
||Special Lecture: Medical Image Computing||Special Lecture: Medical Image Computing||notes
|5/4,6||Contextual Inference in Computer Vision||Project Presentation I|
||Project Presentation II||Project Presentation III||Presentation Due|
||No Class: Term Finished||No Class: Term Finished||Report Due|
Grading (total 102%)
Late Work Policy: Deduct 10% of total score of that assignment per day passing the deadline for each assignment. For example, you submit HW1 one day past the deadline, then the max score you will get is 90% of that assignment. We will not accept late submissions after one week past 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.
Policy on Incomplete: A student will receive an Incomplete if s/he misses either the final project or any two or more of the assignments.
Code of Conduct: Check here for classroom "etiquette" expectations.
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
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: firstname.lastname@example.org; (585) 276-5075; Taylor Hall; www.rochester.edu/college/disability.
Accommodations for COVID-related Illness: Please let me know asap. At the same time, You should contact the College Center for Advising Services (CCAS) or the Graduate Education and Postdoctoral Affairs office. These offices will be able to assist you and refer you to the right resources.