Chenliang Xu

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Fall 2020 - CSC 249/449: Machine Vision

Basic Information

All zoom meetings are password-protected. Please find the information on Blackboard.

Meet Time/Location: T/R 14:00-15:15 on Zoom

Instructor: Prof. Chenliang Xu
Instructor's Office Hours: Tue 15:30-17:00 (By Appointment) on Zoom

TAs and Office Hours:

Dicussion: We will use Piazza for class discussion and communication. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Instead of emailing questions to the teaching staff, I encourage you to use Piazza for all course-related discussions.

Information Flow: We will use Blackboard to make announcements, posting lecture notes and assignments, and submitting student work. The course ID is CSC249.1.FALL2020ASE. 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.


Description

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 in computer vision.

Prerequisites

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.

Hardware/Software Requirements

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:

  1. Using our provided CSUG account to ssh to shared GPU resources; or
  2. Owning an NVIDIA GPU system with at least 6GB GPU memory; or
  3. Renting a cloud GPU via cloud service providers e.g., Google Cloud.

By taking this course, you confirm that you have met the system requirements.


Schedule

This is a tentative schedule of the course and is subject to modifications over the term.
Most lectures will be streamed synchronously via Zoom. Please note, all online 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) HW Due Project
8/25,27 No Class: Before Term Introduction
9/1,3 Linear Filtering and Image Transforms Edges 1 Out
9/8,10 Corners SIFT and Feature Matching
9/15,17 Neural Network and Backpropagation CNN and Feature Visualization 1 Due, 2 Out Teaming Due
9/22,24 Fitting I Fitting II
9/29,10/1 Image Alignment Dense Motion Estimate 2 Due, 3 Out
10/6,8 Segmentation I Segmentation II
10/13,15 Camera Models Stereo Vision Proposal Due
10/20,22 Visual Odometry /
RNN and its Variants
Case Study: Image Captioning and VQA 3 Due, 4 Out
10/27,29 Advanced CNNs I Advanced CNNs II
11/3,5 Study Break Object Recognition I
11/10,12 Object Recognition II Face Recognition 4 Due
11/17,19 Styler Transfer GAN and Image Synthesis
11/24,26 Video Anlaysis Thanksgiving
12/1,3 Visual and Sound Project Presentation I Slides Due
12/8,10 Project Presentation II No Class: Term Finished Report Due

Course Work and Evaluation

Late Work Policy: Deduct 1% of total score of that assignment per hour passing the deadline for each assignment. For example, you submit HW1 12 hours passing the deadline, then the max score you will get is 88% of that assignment. 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.

Policy on Incomplete: The student will receive an Incomplete if he/she missed either the final project or any two or more of the assignments.


Textbook and Other Resources

Machine/Computer Vision is under rapid development over the past few years. There is not a perfect textbook I know of that covers everything needed for an introductory computer vision course in today's context. Even though, we will use the following textbook by Szeliski, which is widely used in many other computer vision courses and is also freely available online.

In addition, I recommend the following textbooks as supplementary reading.

Other Policies

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 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.

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.