CS 566: Introduction to Computer Vision

Office hours

Mohit Gupta	Tuesdays, 11 am — 12 pm (Central), via Canvas Zoom Page
Aniket Rege	Wednesdays and Fridays, 9 am — 10 am. Remote via Canvas Zoom Page until September 23; in-person in Morgridge B2532 thereafter.
Sungjin Cheong	Wednesdays, 1 pm — 2 pm and Fridays, 2 pm — 3 pm, via Canvas Zoom Page

Programming Assignments (55%)

The course will consist of 5 Python programming assignments.

The first homework is introduction to image manipulation in Python.

The homework assignments will be:

Title	Out on	Due	Total points
HW 1	September 3	September 15	5
HW 2	September 22	October 6	16
HW 3	October 6	October 22	13
HW 4	October 29	November 12	7
HW 5	November 12	December 1	14

Guidelines for completing home-work assignments are given here. Please read these carefully.

Homework structure

There will be two types of problems in homeworks: walkthroughs and challenges. Below is a brief description of each problem type:

• walkthrough: these are “lightweight” programming assignments, which essentially walk you through partially complete code and ask you to fill in the missing segments. These assignments are designed with the following two goals:
  1. to help you get started with Python (if you are not yet familiar with it), and
  2. to demonstrate some of the important concepts discussed in class in Python.
• challenge: these are programming challenges to solve a variety of computer vision tasks using Python. In most cases a testing framework or skeleton code will be provided. Your submitted code must work with these.

Late days

You can use up to 7 late days over the whole semester without any grade penalty. Any further delays in any homework submission after your late days are up, will result in a penalty of 20% per late day.

You do not need to notify us when you use late days; we will count them ourselves at the end of the semester. Also note that late days are not rounded up, meaning that if you are only 20 minutes late for a deadline, you still have 6 days, 23 hours, and 40 minutes remaining (Canvas does this tracking automatically).

Project (35%)

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. Some example projects.

You should work on the project in groups of 2-3. 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. Students are encouraged to propose their own project topics, subject to the instructor's approval.

Timeline and what to submit

There will be three checkpoints: a proposal, a mid-term report, and a final project webpage. Create a webpage for the project from 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 "Project Webpage" assignment on Canvas.

• Proposal (5% out of the total 35%, due September 29): 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 rough time table.
• Mid-term report (5%, due October 29): 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 presentation (10%, between December 1 through December 10): this will be a short presentation in class about your project. It will be 5 minutes per team. Please add a link to the presentation on the project webpage.
• Project webpage (15%, due December 10): 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.

Class participation (10%)

This is calculated based on answers to the multiple-choice quizzes on Canvas. Grades depend only on participation, not on correctness.

Readings

The material covered in the course will be available in the presentation slides which will be fairly self-contained. The slides will be available on Canvas. An optional text-book reading is "Computer Vision: Algorithms and Applications", by Richard Szeliski.

Other related courses

• UT-Austin
• Brown
• GATech
• CMU

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.