Course Description: Computer Vision is about empowering computers to visually explore and reason about the world. In this course we will study how images are represented in a computer, how to manipulate them, and how to extract information (features) from images for various applications including image matching, reconstruction, and recognition. We will particulary study basic techniques to recover geometry from images, camera calibration, automatic image alignment, boundary detection, object recognition, image classification, and image retrieval/search. We will develop the intuitions, study the mathematical methods, and learn to use these in practical computer vision systems.

Learning Objectives: (a) Develop intuitions between Human Vision and Computer Vision, (b) Understanding the basics of 2D and 3D Computer Vision, (c) Become familiar with the technical approaches in computer vision such as registration, matching, and recognition, and (d) Obtain practical experience in the implementation of computer vision applications.

Textbook: There is no required textbook, but you're strongly encouraged to read chapters from the following book: "Computer Vision: Algorithms and Applications" by Richard Szeliski. This is especially important for the first half of the class. The book is available for free online or available for purchase. Also optional readings from David Forsyth & Jean Ponce's "Computer Vision: A Modern Approach".

Syllabus

Prerrequisites: This course requires no specific previous background. You need to know about matrices, calculating derivatives, and probabilities (bayes rule). You will also need to be at least a moderately proficient programmer in python. There will be several small project assignments. These projects will show you the basics of modern computer vision algorithms, and will give you the tools for implementing more advanced ones. There will also be two quizzes. Being proficient in Python by completing a few projects in this language before the class starts is helpful. You should install python and jupyter.

Grading:

• Assignments: 50% (5 assignments: 10% + 10% + 10% + 10% + 10%)
• Quizzes: 20% (2 written quizzes: 10% + 10%)
• Final Project: 30% (2-page report + code + any optional supplementary material to showcase your work)

Late Submission Policy: You will lose 10% for each day late for the programming projects. This means anytime within the first 24 hours after the due date count as 1 full day, up to 48 hours is two and 72 for the third late day. However, you will have three late days for the whole course to address any unexpected traveling, other commitments, interviews, hackathons, etc. These three late day grace period will be accounted at the end of the semester. Please do not send the professor or the TAs emails about submitting late assignments, you do not need to explain the reason for submitting a late assignment, please just submit the late assignment. If you don't use more than three late days, you will not be penalized in any way (but this will be accounted at the end of the semester so you will not see points deducted until then). Note: the 3-day grace period doesn't apply to the course project, and it doesn't count fractions of a day, only full days (if it shows late on Collab, it is late, and any fraction of a day counts as a full day). Note: Assignments on Collab will not accept submissions indefinitely, I can only guarantee that they will accept submissions during the next three days following the deadline. In summary, if this late policy is confusing, please just don't submit late assignments, this is a large class and any emails regarding this matter are not productive use of time for the staff.

Academic Integrity "The School of Engineering and Applied Science relies upon and cherishes its community of trust. We firmly endorse, uphold, and embrace the University’s Honor principle that students will not lie, cheat, or steal, nor shall they tolerate those who do. We recognize that even one honor infraction can destroy an exemplary reputation that has taken years to build. Acting in a manner consistent with the principles of honor will benefit every member of the community both while enrolled in the Engineering School and in the future. Students are expected to be familiar with the university honor code, including the section on academic fraud." Note from the instructor: Anything you submit I will by default assume it is something you are claiming credit as your own, and if not, you should state so explicitly. For course projects and assignments, it has to be clear what was done by you and was not.

Accessibility Statement: "The University of Virginia strives to provide accessibility to all students. If you require an accommodation to fully access this course, please contact the Student Disability Access Center (SDAC) at (434) 243-5180 or sdac@virginia.edu. If you are unsure if you require an accommodation, or to learn more about their services, you may contact the SDAC at the number above or by visiting their website at https://www.studenthealth.virginia.edu/student-disability-access-center/about-sdac ."

Other similar courses that might be of interest and from which I borrowed a lot of material:

• Introduction to Computer Vision (Dimitris Samaras, Stony Brook University)
• Computer Vision (James Hays, Georgia Institute of Technology)
• Computer Vision (Svetlana Lazebnik, University of Illinois at Urbana Champaign)
• Computer Vision: Foundations and Applications (Juan Carlos Niebles and Ranjay Krishna, Stanford University)
• Computer Vision (Jan-Michael Frahm, University of North Carolina at Chapel Hill)