CS 4476 / 6476 Computer Vision

Fall 2017, MWF 11:15 to 12:05, College of Computing room 16
Instructor: James Hays

TAs: Varun Agrawal, Samarth Brahmbhatt, Cusuh Ham, Eunji Chong, Wenqi Xian, Wengling Chen, Albert Shaw, Stefan Stojanov, Jonathan Balloch

Computer Vision, art by kirkh.deviantart.com

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 develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment, tracking, boundary detection, and recognition. 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.

The difference between the undergraduate version of the class (CS4476) and the graduate version (CS6476) will be the requirements on the projects. In particular, more challenging extensions of the projects will be extra credit for CS4476 but required for CS6476.

The Advanced Computer Vision course (CS7476) in spring will build on this course and deal with advanced and research related topics in Computer Vision, including Machine Learning, Graphics, and Robotics topics that impact Computer Vision.

Learning Objectives

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

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:

Grading

Your final grade will be made up from You will lose 10% each day for late projects. However, you have three "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.

Graduate Credit

If you are enrolled in the graduate section CS 6476 then you will be expected to do additional work on each project. Each project will list several extra credit opportunities available and CS 6476 students will be required to do at least 10 points worth of extra credit (for which you will not get extra credit, unless you do more than 10 points worth).

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.

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).

Contact Info and Office Hours:

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

Assignments

Highlighted projects

All Results

(Optional) Eclipse viewing
Image Filtering and Hybrid images Project 1 results
Local Feature Matching
Camera Calibration and Fundamental Matrix Estimation with RANSAC
Scene Recognition with Bag of Words
Face Detection with a Sliding Window
Deep Learning
It is strongly recommended that all projects be completed in Matlab. All starter code will be provided for Matlab. Students may implement projects through other means but it will generally be more difficult.

Textbook

Readings will be assigned in "Computer Vision: Algorithms and Applications" by Richard Szeliski. The book is available for free online or available for purchase.

Syllabus

Class Date Topic Slides Reading Projects
Mon, Aug 21 No Lecture Optional assignment 0, eclipse pinhole photography
Wed, Aug 23 Introduction to computer vision pptx, pdf Szeliski 1 Project 1 out
Image Formation and Filtering (Szeliski chapters 2 and 3)
Fri, Aug 25 Cameras and Optics pptx, pdf Szeliski 2.1, especially 2.1.5
Mon, Aug 28 Light and Color pptx, pdf Szeliski 2.2 and 2.3
Wed, Aug 30 Image Filtering pptx, pdf Szeliski 3.2
Fri, Sept 1 Thinking in frequency pptx, pdf Szeliski 3.4
Mon, Sept 4 No classes, Institute holiday
Wed, Sept 6 Thinking in frequency part 2 pptx, pdf Szeliski 3.5.2 and 8.1.1
Feature Detection and Matching
Fri, Sept 8 Edge detection pptx, pdf Szeliski 4.2
Mon, Sept 11 No classes, Tropical Storm Irma Project 2 out
Wed, Sept 13 Interest points and corners pptx, pdf Szeliski 4.1.1
Fri, Sept 15 Local image features pptx, pdf Szeliski 4.1.2
Mon, Sept 18 Feature matching and hough transform pptx, pdf Szeliski 4.1.3 and 4.3.2
Wed, Sept 20 Model fitting and RANSAC pptx, pdf Szeliski 6.1 and 2.1
Multiple Views and Motion
Fri, Sept 22 Stereo intro Szeliski 11
Mon, Sept 25 Camera Calibration Szeliski 6.2.1
Wed, Sept 27 Epipolar Geometry and Structure from Motion Szeliski 7 Project 3 out
Fri, Sept 29 Feature Tracking and Optical Flow Szeliski 8.1 and 8.4
Machine Learning Crash Course
Mon, Oct 2 Machine learning: unsupervised learning Szeliski 5.3
Wed, Oct 4 Machine learning: Supervised learning Szeliski 5.3
Fri, Oct 6 Quiz 1
Recognition
Mon, Oct 9 No classes, Institute holiday
Wed, Oct 11 Recognition overview and bag of features Szeliski 14 Project 4 out
Fri, Oct 13 TBD
Mon, Oct 16 Large-scale instance recognition Szeliski 14.3.2
Wed, Oct 18 Large-scale instance recognition, continued
Fri, Oct 20 Large-scale category recognition and advanced feature encoding
Mon, Oct 23 Detection with sliding windows: Viola Jones Szeliski 14.1 and 14.2
Wed, Oct 25 Detection with sliding windows: Dalal Triggs Szeliski 14.1
Fri, Oct 27 Pascal VOC and Big Data Szeliski 14.5 Project 5 out
Mon, Oct 30 Big Data 2
Wed, Nov 1 Human computation and crowdsourcing
Fri, Nov 3 Attributes and more crowdsourcing
Mon, Nov 6 Modern boundary detection and Sketches Szeliski 4.2
Wed, Nov 8 Context, Spatial Layout, and scene parsing
Deep Learning
Fri, Nov 10 Neural networks
Mon, Nov 13 Convolutional networks for recognition Project 6 out
Wed, Nov 15 Object Detectors Emerge in Deep Scene CNNs
Fri, Nov 17 Deep Geolocalization
Mon, Nov 20 MS COCO and Deeper Deep Architectures
Wed, Nov 22 No classes, Institute holiday
Fri, Nov 24 No classes, Institute holiday
Mon, Nov 27 Structured output from Deep Learning
Wed, Nov 29 "Unsupervised" Learning and Style Transfer
Fri, Dec 1 Quiz 2
Mon, Dec 4 No classes, final instructional period
Wed, Dec 6 No classes, reading period
Final Exam Period - not used

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.