Syllabus
Course Information
Course Structure
This course will be remote synchronous. Lectures will be recorded and made available within 24 hours of original lecture slot.
Problem Sets: Each assignment will have components required as well as extra credit.
You may discuss the problem sets with other students, but your submission (both text and code) must be individual work.
Quizzes: This course will have online quizzes. These are designed to test your understanding of the material. They will be timed, available through Canvas.
You may use course material during the exam, but your submission must be individual.
This is a lecture-based course with project/coding assignments and two exams. Students are expected to attend lectures and participate in discussions.
Learning Objectives
Upon completion of this course, students should be able to:
Recognize and describe both the theoretical and practical aspects of computing with images. Connect issues from Computer Vision to Human Vision
Describe the foundation of image formation and image analysis. Understand the basics of 2D and 3D Computer Vision.
Become familiar with the major technical approaches involved in computer vision. Describe various methods used for registration, alignment, and matching in images.
Get an exposure to advanced concepts, including state of the art deep learning architectures, in all aspects of computer vision.
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.
Formal prerequisites require taking the following courses
Satisfy one of the following:
Satisfy one of the following:
Undergraduate Semester level MATH 2605 Minimum Grade of D
Undergraduate Semester level MATH 2401 Minimum Grade of D (or T)
Undergraduate Semester level MATH 2411 Minimum Grade of D (or T)
Undergraduate Semester level MATH 1553 Minimum Grade of D (or T)
Undergraduate Semester level MATH 1554 Minimum Grade of D (or T)
Undergraduate Semester level MATH 1564 Minimum Grade of D (or T)
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 citations. 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 exams, 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 opportunities may build on third party data sets or code. That’s acceptable. Feel free to include results built on other software, as long as what you hand in clearly cites the third-party source, making it clear 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 accommodations for students with documented disabilities. These accommodations must be arranged in advance and in accordance with the ADAPTS office policies (www.adapts.gatech.edu).
Important Links:
Piazza. This should be your first stop for questions and announcements.
canvas.gatech.edu will be used to take quizzes, view grades, and view assignments.
Gradescope will be used to hand in assignments.
Grading
Problem Sets (90% final grade)
6 problem sets each worth 15% of your final grade.
Quizzes (10% final grade)
There will be 2 Quizzes each worth 5% of your final grade.
Quizzes will taken through canvas, open notes, but timed.
Pass/Fail: If you wish to take the course pass/fail you need to obtain >=70% total across all assignments and exams.
Auditing: Auditing will not be permitted this semester due to the online course format. However, course content will mainly be accessible on this website with gatech credentials.
Calculate your grade
We will use the following cutoffs: >=90 (A), >=80 (B), >=70 (C), >=60 (D), <60 F
Due Dates
All problem sets/reports are to be submitted by the due date noted on the assignment. Deadlines are firm. Anything from 1 second to 24 hours is one day late.
Late Day Policy
Throughout the term, you have an allowance of four seven (as of 3/12/21) free late days for your submissions, meaning you can accrue up to four days in late submissions with no penalty. For example, you could turn in one assignment four days late, or four problem sets each one day late. Once you have used all your free late days, a late submission will not be accepted and will be awarded 0 credit. Please plan ahead so you can spend your late days wisely. In particular, note that we expect you will find the earlier assignments easier than those later in the course. A submission is considered one day late if is submitted 1 second to 24 hours late.
Acknowledgements
The materials from this class rely significantly on slides prepared by other instructors, especially Devi Parikh, Frank Dellaert, Kristen Grauman, David Fouhey, 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.