CS 766 Computer Vision (Spring 2017)
MW 9:30am - 10:45am, Humanities 1651



Instructor: Mohit Gupta (mohitg@cs.wisc.edu)        Office Hours: Mon 11am-12pm in CS 6395

TA: Felipe Gutierrez (fgutierrez3@wisc.edu)             Office Hours: Tues 12pm-2pm in CS 1307

Discussion Group: Piazza. A discussion for each homework assignment will be created on Piazza. Please post all of your questions on the discussion board so that others may learn from your questions as well. Do not email the professor or TA directly with homework questions.


Course Description: The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos.


This course will provide an introduction to computer vision, including such topics as image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. This is a project-based course, in which you will implement several computer vision algorithms and do a final project on a research topic of your choice.

 

Image courtesy James Hays’s Vision Course Website (http://cs.brown.edu/courses/cs143/)

 




Prerequisites
This course will be self-contained; students do not need to have computer vision background. This course will assume a reasonable knowledge of linear algebra and calculus as a prerequisite. The programming assignments will be in MATLAB, so a familiarity with MATLAB is essential.
Please send me email or speak to me if you are unsure of whether you can take the course.

 

Tentative Syllabus

 

Class Date

Topic

Home-works and Project

M, Jan 23

Pre-Introduction: Fun with Optical Illusions

W, Jan 25

Introduction to Computer Vision

M, Jan 30

Image Formation

HW-1 out

W, Feb 1

Image Sensing

M, Feb 6

Binary Images and Processing

W, Feb 8

Image Processing-I – Basic Image Filtering

HW-1 due, HW-2 out

M, Feb 13

Image Processing-II – Fourier Domain Image Filtering

W, Feb 15

Edge Detection

Project Proposal due

M, Feb 20

Boundary Detection

W, Feb 22

Buffer lecture

HW-2 due, HW-3 out

M, Feb 27

2D Object Recognition Using SIFT

HW-2 due, HW-3 out

W, Mar 1

Image Alignment

F, Mar 3

Face Detection

M, Mar 6

Image Segmentation

HW-3 due, HW-4 out

W, Mar 8

Radiometry and Reflectance

 

F, Mar 10

Photometric Stereo + Shape From Shading

M, Mar 13

No class

 

W, Mar 15

No class

F, Mar 17

No class

HW-4 due, HW-5 out

M, Mar 20

No class: Spring Break

 

W, Mar 22

No class: Spring Break

M, Mar 27

Shape From Focus/Defocus

W, Mar 29

Camera Calibration and Shape From Stereo

M, Apr 3

Shape From Uncalibrated Stereo

Project Mid-Term Report due

W, Apr 5

Structure From Motion

HW-5 due, HW-6 out

F, Apr 7

Optical Flow and Motion Measurement

M, Apr 10

Image Tracking

W, Apr 12

Shape From Structured Light and Time-of-Flight

HW-6 due, HW-7 out

M, Apr 17

Neural Networks and Deep Learning

W, Apr 19

Selected Advanced Topics: Computational Cameras

M, Apr 24

Project Presentations

W, Apr 26

Project Presentations

F, Apr 28

Project Presentations

 

M, May 1

Project Presentations

HW-7 due

W, May 3

Project Presentations

M, May 8

Project Webpage due

 

Coursework and Grading

Grading will be based on a 100 point system. There are two main components: (a) Home-works (60% grade), and (b) Final research project (40% grade). Details about these components are given below.

 

 

Home-work Assignments

The course will consist of 7 homework assignments. All home-works together will account for 60% of your final grade. Some home-works are lighter than others; their relative weights in your final homework grade are determined by the total number of points listed at the top of each home-work. Guidelines for completing home-work assignments are given here. Please read these carefully.

  

A discussion for each homework assignment will be created on Piazza. Please post all of your questions on the discussion board so that others may learn from your questions as well. Do not email the professor or TA directly with homework questions.

 

Include all the files in a zip file named hwX_yourNetID.zip (where X is the homework number) and upload the zip file to Canvas. All home-works are to be submitted by 9:30am on the due date. Only for the home-works (not project), students will be allowed a total of 5 (five) late days per semester; each additional late day will incur a 20% penalty.

 

 

Final Research Project

The final project is research-oriented. It can be a pure vision project or an application of vision technique in the student's own research area. You are expected to implement one (or more) related research papers, or think of some interesting novel ideas and implement them using the techniques discussed in class. Some possible project ideas are listed on this page. However, you are welcome to come up with your own ideas - in fact we encourage this. Students can propose their own project topics, subject to the instructor's approval.

 

You should work on the project in groups of two. In your submission, please clearly identify the contribution of both group members. Please note that members in the same group will not necessarily get the same grade.

 

Project Timeline and What to Submit: There will be three checkpoints: a project proposal, an intermediate milestone report, and a final project webpage. Create a webpage for the project at the beginning. This page will include the proposal, the mid-term report, interesting implementation details, some results (e.g., images, videos, etc.), and so on. Your website should also include downloadable source and executable code. Do not modify the website after the due date of the assignment. Also, send an email to the instructor and TA with the URL of your webpage BEFORE the due dates.

 

Project Proposal (Due: Feb 15) (5%)

This will be a short report (usually one or two pages will be enough). You will explain what problem you are trying to solve, why you want to solve it, and what are the possible steps to the solution. Please include a time table.

 

Project Mid-Term Report (Due: March 29) (5%)

In this report, you will need to give a brief summary of current progress, including your current results, the difficulties that arise during the implementation, and how your proposal may have changed in light of current progress.

 

Final Project Presentations (April 24, April 26, April 28, May 1, May 3) (15%)

This will be a short presentation in class about your project. It will be 10 minutes per team. Please add a link to the presentation on the project webpage.

 

Project Webpage (Due: May 8) (15%)

Assemble all the materials you have finished for your project in a webpage or wiki, including the motivation, the approach, your implementation, the results, and discussion of problems encountered. If you have comparison results or some interesting findings, put them on the webpage and discuss these as well.

 

Readings
The material covered in the course will be available in the presentation slides which will be fairly self-contained. The slides will be made available as printed copies before the start of every class. The slides will not be available electronically. Optional text-book readings include Computer Vision: Algorithms and Applications, by Richard Szeliski. An online version is available, or you may purchase the book at a variety of locations. Also, see Robot Vision by Berthold KP Horn and Optics by E. Hecht.

Acknowledgments
The materials from this class rely significantly on slides prepared by other instructors, especially Shree Nayar, Oliver Cossairt, Peter Belhumeur and Alyosha Efros. Also see other similar courses by Fredo Durand and Bill Freeman (MIT), Peter Belhumeur (Columbia), Irfan Essa (Georgia Tech.), Steve Seitz (U. Washington) and Kyros Kutulakos (U. Toronto). The instructor is extremely thankful to the researchers for making their notes available online.

Academic Integrity
This course follows the University of Wisconsin-Madison Code of Academic Integrity. Unless specifically authorized by the instructor, all coursework is to be done by the student working alone. Violations of the rules will not be tolerated.