Time/Place: TR 12:30-13:45 in
473 HUTCH B&L 106
Instructor: Prof. Chenliang Xu
Instructor's Office Hours: Tue 14:15-15:00, 3005 Wegmans
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.
Please check pre-requisites on the official course listing page. Some knowledge of linear algebra and basic statistics is helpful but we will review these subjects in the class. For assignments and projects, the most common programming languages are MATLAB and Python. Note that MATLAB provides an image processing toolbox and is available on most CS department undergraduate and research networks. CSC 449, a graduate-level course, has higher expectations / standards than CSC 249, an undergraduate-level course, for completing course project.
This is a tentative schedule of the course and will be populated over the semester.
Course materials will be posted on Blackboard.
Blue: lectures; Yellow: exams/presentations; Grey: no class or cancelled classes; Green: all others.
|2||1/23,25||Camera Models||FP 1|
|3||1/30,2/1||Color, Light and Shading||FP 2, 3|
|4||2/6||MATLAB/Linear Algebra Review||HW1 Out|
|4||2/8||Linear Filtering||FP 4|
|5||2/15||Edges and Corners||FP 5|
|6||2/20,22||SIFT, Segmentation I||Lowe'04, FP 9|
|7||2/27,3/1||Segmentation II, Grouping and Fitting I||FP 10, SZ 4.3, 6.1|
|8||3/6,8||Grouping and Fitting II, Linear Classifier I||HW2 Out|
|9||3/13,15||Spring Recess||HW1 Due (Extended)|
|10||3/22||Linear Classifier II|
|11||3/27,29||Convolutional Neural Network||2-Page Proposal Due,
|13||4/10||Guest Lecture by Prof. Zhiyao Duan|
|16||5/1||Project Presentation||HW3 Due|
This course primarily uses Blackboard to manage information flow. The course ID is CSC249.2018SPRING.49702. It will be used for announcements, posting lecture notes and assignments, discussions and submitting work. The instructor's course website contains an outline of course schedule, which will be updated over the term, and also serves for the purpose of a syllabus.
All assignments should be submitted on Blackboard and no late work will be accepted.
Textbook: Although we do not have a mandatory textbook, the following books are recommended.
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 at here. You are expected to read, understand and follow the policy.
Disabilities: If you need an accommodation based on the impact of a disability, please contact me privately during the first two weeks to discuss your specific needs. Alternatively, contact Center for Excellence in Teaching and Learning at 585-275-9049 in room 1-154 Dewey Hall to coordinate reasonable accommodations.