Introduction to Computer Vision

Syllabus

Learning Objectives

Upon completion of this course, students should be able to:

1. Recognize and describe both the theoretical and practical aspects of computing with images. Connect issues from Computer Vision to Human Vision
2. Describe the foundation of image formation and image analysis. Understand the basics of 2D and 3D Computer Vision.
3. Become familiar with the major technical approaches involved in computer vision. Describe various methods used for registration, alignment, and matching in images.
4. Get an exposure to advanced concepts, including state of the art deep learning architectures, in all aspects of computer vision.
5. Build computer vision applications with python and the pytorch framework.

Prerequisites

No prior experience with computer vision is assumed, although previous knowledge of visual computing or signal processing will be helpful. The following skills are necessary for this class:

• Data structures: You’ll be writing code that builds representations of images, features, and geometric constructions.
• Programming: Projects are to be completed and graded in Python. All project starter code will be in Python. TA’s will support questions about Python. If you’ve never used Python that is OK, as long as you have programming experience.
• Math: Linear algebra, vector calculus, and probability. Linear algebra is the most important and students who have not taken a linear algebra course have struggled in the past.

Grading

This project is heavily project-based, but there will be a midterm and final, each covering about half of the material. The grading distribution is:

Late policy for this course will be 20% per day late, as discussed on the first day of class and clarified on Piazza.

Academic Integrity

Academic dishonesty will not be tolerated. This includes cheating, lying about course matters, plagiarism, or helping others commit a violation of the Honor Code. Plagiarism includes reproducing the words of others without both the use of quotation marks and citation. Students are reminded of the obligations and expectations associated with the Georgia Tech Academic Honor Code and Student Code of Conduct, available online at www.honor.gatech.edu. For quizzes, no supporting materials are allowed (notes, calculators, phones, etc).

You are expected to implement the core components of each project on your own, but the extra credit opportunties often build on third party data sets or code. That’s fine. Feel free to include results built on other software, as long as you are clear in your handin that it is not your own work.

You should not view or edit anyone else’s code. You should not post code to Piazza, except for starter code / helper code that isn’t related to the core project.

Learning Accommodations

If needed, we will make classroom accommodations for students with documented disabilities. These accommodations must be arranged in advance and in accordance with the ADAPTS office (www.adapts.gatech.edu).

Important Links:

• Piazza. This should be your first stop for questions and announcements.
• canvas.gatech.edu will be used to hand in assignments.

Contact Info and Office Hours:

If possible, please use Piazza to ask questions and seek clarifications before emailing the instructor or staff.

• Cusuh Ham cusuh@gatech.edu
• Ayush Baid abaid@gatech.edu
• Jacob Knaup jknaup3@gatech.edu
• Julia Chen julia.c@gatech.edu
• John Lambert johnlambert@gatech.edu
• Jon Balloch balloch@gatech.edu
• Patsorn Sangkloy patsorn_sangkloy@gatech.edu
• Shenhao Jiang jiangshenhao@gatech.edu
• Stefan Stojanov sstojanov@gatech.edu
• Vijay Upadhya vupadhya6@gatech.edu

Acknowledgements

The materials from this class rely significantly on slides prepared by other instructors, especially James Hays, Derek Hoiem and Svetlana Lazebnik. Each slide set and assignment contains acknowledgements. Feel free to use these slides for academic or research purposes, but please maintain all acknowledgements.