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
This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. We'll explore methods for depth recovery from stereo images, camera calibration, automated alignment, tracking, boundary detection, and recognition. We'll use both classical machine learning and deep learning to approach these problems. The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the projects.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 leading to object categorization and segmentation in images.
- 5. Build computer vision applications.
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 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- 90% 6 programming projects. First project 14% of grade, projects 2 through 5 19% of grade, and project 6 extra credit.
- 10% Two open book problem sets
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. We will have two open book problem sets reviewing lecture material.
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 day. 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 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 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. For archived recordings of lecture
- Piazza. For discussion
- Gradescope. For project and problem set handin
Contact Info and Office Hours:
If possible, please use Piazza to ask questions and seek clarifications before emailing the instructor or staff.- James: hays[at]gatech.edu
- Cusuh Ham: cusuh[at]gatech.edu
- Yijun "Esther" Gu: yjgu[at]gatech.edu
- Anant Joshi: anant.joshi[at]gatech.edu
- Arvind Krishnakumar: akrishna[at]gatech.edu
- John Lambert: johnlambert[at]gatech.edu
- Vijay Upadhya: vupadhya6[at]gatech.edu
- Jing Wu: jingwu[at]gatech.edu
- James, Tuesday, 2 to 3 on Bluejeans.
- TA hours: TBD.
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
| Class Date | Topic | Slides | Reading | Projects |
| Mon, Jan 18 | No classes, Institute holiday | |||
| Wed, Jan 20 | Introduction to computer vision | pdf, pptx | Szeliski 1 | Project 1 out |
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| Mon, Jan 25 | Camera Projection and Image Filtering | pdf, pptx | Szeliski 2.1, especially 2.1.4 | |
| Wed, Jan 27 | Light and Color and Sensors | pdf, pptx | Szeliski 2.2 and 2.3 | |
| Mon, Feb 1 | Thinking in Frequency | pdf, pptx | Szeliski 3.2 and 3.4 | |
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| Wed, Feb 3 | Interest points and corners | pdf, pptx | Szeliski 7.1.1 and 7.1.2 | |
| Mon, Feb 8 | Local image features | pdf, pptx | Szeliski 7.1.3 | |
| Wed, Feb 10 | Model fitting, Hough Transform | pdf, pptx | Szeliski 7.4.2 and 2.1 | |
| Mon, Feb 15 | RANSAC and transformations | pdf, pptx | Szeliski 8.1 and 2.1 | |
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| Wed, Feb 17 | Stereo intro | pdf, pptx | Szeliski 12 and 11.2.1 | |
| Mon, Feb 22 | Camera Calibration, Epipolar Geometry, and Structure from Motion | pdf, pptx | Szeliski 11 | |
| Wed, Feb 24 | Dense Stereo Correspondence | pdf, pptx | Szeliski 12 | |
| Mon, Mar 1 | No class | |||
| Wed, Mar 3 | Optical Flow | pdf, pptx | Szeliski 9.4 | |
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| Mon, Mar 8 | Machine learning crash course | pdf, pptx | Szeliski 5.1 and 5.2 | |
| Wed, Mar 10 | Machine learning crash course, part 2 | pdf, pptx | Szeliski 5.1 and 5.2 | |
| Mon, Mar 15 | Classical recognition techniques and neural network intro | pdf, pptx | Szeliski 6.2.1 and 5.3 | |
| Wed, Mar 17 | Convolutional Networks | pdf, pptx | Szeliski 5.4 | |
| Mon, Mar 22 | Object Detectors Emerge in Deep Scene CNNs and Deeper Deep Architectures. | pdf, pptx | ||
| Wed, Mar 24 | No classes, Institute holiday | |||
| Mon, Mar 29 | ResNet, Big Data | pdf, pptx | ||
| Wed, Mar 31 | Crowdsourcing | pdf, pptx | Szeliski 6.3 | |
| Mon, Apr 5 | "Unsupervised" Learning and Colorization | pdf, pptx | ||
| Wed, Apr 7 | Deep Object Detection and Structured Output from Deep Networks | pdf, pptx | Szeliski 5.4.7 | |
| Mon, Apr 12 | 3D Point Processing and Lidar | pdf, pptx | ||
| Wed, Apr 14 | Semantic and Panoptic Segmentation | pdf, pptx | Szeliski 5.5 | |
| Mon, Apr 19 | Transformer architectures | pdf, pptx | ||
| Wed, Apr 21 | No lecture | |||
| Mon, Apr 26 | No lecture, "final instructional class days" | |||
| Wed, Apr 28 | No lecture, "reading period" | |||
| Final Exam Period | Not used. No class or final exam | |||