CS 766 Computer Vision (Spring 2018)
MW 8am - 9:15am, COMP SCI 1221

Instructor: Mohit Gupta (mohitg@cs.wisc.edu)        Office Hours: Mon 3pm-4pm in CS 6395

TA: Anant Gupta (agupta225@wisc.edu)             Office Hours: Tues 3pm-5pm in CS 3367

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/)


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


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Class Date


Home-works and Project

W, Jan 24

Introduction and Fun With Optical Illusions

M, Jan 29

Image Formation

HW-1 out

W, Jan 31

Image Sensing

M, Feb 5

Binary Images and Processing

W, Feb 7

Image Processing-I – Basic Image Filtering

HW-1 due, HW-2 out

M, Feb 12

Image Processing-II – Fourier Domain Image Filtering

W, Feb 14

Edge Detection

Project Proposal due

M, Feb 19

Boundary Detection

W, Feb 21

2D Object Recognition Using SIFT

HW-2 due, HW-3 out

M, Feb 26

Image Alignment

W, Feb 28

Face Detection

M, Mar 5

Image Segmentation

HW-3 due, HW-4 out

W, Mar 7

Radiometry, Reflectance and Photometric Stereo


M, Mar 12

No class

W, Mar 14

No class

F, Mar 16

Shape from Focus / Defocus

M, Mar 19

Camera Calibration and Shape From Stereo

HW-4 due, HW-5 out

W, Mar 21

Shape From Uncalibrated Stereo

M, Mar 26

No class: Spring Break


W, Mar 28

No class: Spring Break

M, Apr 2

Structure From Motion

Project Mid-Term Report due

W, Apr 4

Optical Flow and Motion Measurement

HW-5 due, HW-6 out

F, Apr 6

Image Tracking

M, Apr 9

Neural Networks and Deep Learning (Guest Lecture)

W, Apr 11

Modern 3D Cameras: Structured Light and Time-of-Flight

F, Apr 13

Selected Advanced Topics: Computational Cameras

M, Apr 16

No class

HW-6 due, HW-7 out

W, Apr 18

No class

M, Apr 23

Project Presentations

W, Apr 25

Project Presentations

F, Apr 27

Project Presentations


M, Apr 30

Project Presentations

HW-7 due

W, May 2

Project Presentations

F, May 4

Project Presentations

M, May 7

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 8am 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 2-3. 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 14) (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: April 2) (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 23, April 25, April 27, April 30, May 2, May 4) (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 7) (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.


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.

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.