CS 6476 Computer Vision

Spring 2021, MW 12:30 to 1:45, Synchronous remote lecture on Bluejeans

Instructor: James Hays

TAs: Cusuh Ham (head TA), Anant Joshi. Arvind Krishnakumar, John Lambert, Vijay Upadhya, Jing Wu

Course Description

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 maior technical approaches involved in computer vision. Describe various methods used for registration, alignment, and matching in images.

•   4. Get an exposure to advanced concepts leading to object categorization and segmentation in images.

• 5. Build computer vision applications.

Prerequisites

No prior experience with computer vision is assumed. although previous knowledee 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 and PyTorch. 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

Your final grade will be made up from . 

•   100% 6 programming projects

This course traditionally has in person quizzes, but we will instead place more emphasize on the projects this semester because of the difficulty in remotely administering exams.

You will lose 10% each day for late projects. However, you have six "late days" for the whole course. That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late dav. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible.

These late days are intended to cover unexpected clustering of due dates, travel commitments. interviews. hackathons etc. Don't ask for extensions to due dates because we are already giving you a pool of late days to manage yourself. In fact, we're doubling the pool of late days this semester because of the difficult circumstances.

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. We will use tools to find code sharing in projects.

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 vour handin that it is not your own work.

You should not view or edit anvone else's code. You should not post code to Canvas. 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 office of disability services. (disabilityservices.gatech.edu).

Important Links:

•   Canvas. This should be your first stop for questions and announcements. It will also be used for project handin.

Contact Info and Office Hours:

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

•   James: haysfat]gatech.edu

•   Cusuh Ham: cusuhfat gatech.edu

•   Anant Joshi: anant.joshifat]gatech.edu

•   Arvind Krishnakumar: akrishna[atlgatech.edu

•   John Lambert: johnlambert[atlgatech.edu

•   Vijay Upadhya: vupadhya6[atlgatech.edu

•   Jing Wu: jingwufatlgatech.edu

Office Hours

•   James, Tuesday, 2 to 3 on Bluejeans

•   TA hours: TBD

Projects                                             Highlighted projects                                             All Results

Convolution and Hybrid images

SIFT Local Feature Matching

Camera Calibration and Fundamental 

Matrix Estimation with RANSAC

Stereo

Recognition with deep learning

Semantic Segmentation

All starter code and projects will be in Python with the use of various third party libraries. We will make an effort to support MacOS, Windows, and Linux. The course does not teach python and assumed you have enough familiarity wit procedural programming languages to complete the projects.

Textbook

Readings will be assigned in "Computer Vision: Algorithms and Applications, 2nd edition" by Richard Szeliski.This semester is our first time using the 2nd edition of the book. The book is available for free online or available for purchase.

Syllabus

Acknowledgements

The materials from this class rely significantly on slides prepared by other instructors, especially 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.