Computer Vision
CSCI 5520G
Fall 2020
Faisal Qureshi
faisal.qureshi@ontariotechu.net

News

Oct 8, 2020
No lectures next week. It is reading week.
Sep 10, 2020
Please see this pip requirements file to set up your python programming environment. Please note that this file is generated on my OSX machine. So you may need to remove some mac specific packages. The important packages are opencv, opencv-contrib, numpy, scipy, jupyter echo system, torch and torchvision.
Sep 10, 2020
Piazza is now available. Check canvas. If you haven't received an invite from Piazza, please check your spam and contact me. We will use Piazza for all course specific discussions.
Sep 10, 2020
Please check canvas for link to Google shared folder that contains shared materials.
Sep 9, 2020
Please check canvas for link to Google shared folder that contains lecture recordings.
Aug 23, 2020
Website is now online.

Course Info

Instructor

Faisal Qureshi

Email: faisal.qureshi@ontariotechu.net
Office: UA4032

Discussion Group

We will be using course Canvas for online communication.

Office hours

  • Tue, 11 - 12 am in Online, check course canvas
  • Or by appointment

Lectures

  • W 2:10-5:00 in Online, check course canvas

Syllabus

Canvas (requires login)

Course notes

Description

This is an introductory graduate course in computer vision. The course will focus on computer vision theory and applications.

Computer vision deals with processing and analyzing digital images to extract useful properties about the real world. Computer vision, for example, can be used to extract 3D scene structure from a given set of photos, recognize people in images, identify actions in a video sequence, etc. Computer vision has also been used in specialized domains, such as medical imaging, say for analyzing CT scans or MRI photographs, satellite imaging, say for analyzing the health of a an ecosystem, etc. Computer vision has also found wide-spread use in entertainment and gaming industry.

Solving computer vision, it turns out, is a tough problem. Digital images after all are little more than a collection of pixels. Recent advances in machine learning, especially in deep learning, has opened up new avenues for computer vision research. The goal is simple: design algorithms and systems that will enable a computer to “learn to see” by “looking” at example pictures and videos. With this in mind, this course will also briefly explore machine learning approaches that have found wide-spread use in computer vision applications.

This course will mix lectures on a selection of topics with paper reading and discussion. The topics are selected to help you understand and implement the papers that you are asked to read, present, and discuss. The first 45 minutes of most classes will be devoted to lectures on one of the selected topics. The remain time will be used for paper presentation and discussion. The course will cover the following topics:

These topics provides a decent basis for understanding the papers that we plan to read and discuss in this course.

Pre-requisites

The course assumes that students are comfortable with statistics, basic linear algebra, and programming.

We will be using Python for the programming part of this course. For Python, I recommend the Anaconda distribution, which comes pre-loaded for nearly all the packages that we will be using in this course. Of course you are welcome to use any variant/distribution of Python that suits you.

The course also assumes that students are willing to read and comprehend large volumes of technical papers. Furthermore, that students have some experience with technical report writing.

Grading

Important dates

Ontario Tech University’s academic calendar that lists important dates (and deadlines) is available at here.

Course notes

Course calendar

The list of assigned papers will be available after the first week of classes. Please check the course website for details.

Course Work

Presentation

Each student will be assigned recent papers to read and present. The student will be responsible for leading the discussion for this paper. Each student may be assigned to present multiple papers.

Instructions for the presenter

Instructions for the participants

Project

The course project is an independent exploration of a specific problem within the context of this course. A project can be implementation oriented—where a student implements a computer vision system—or application oriented—where a student attempts to solve a problem (of suitable difficulty) by applying machine learning techniques. The project topic will be selected in consultation with the instructor.

Project grade will depend on the ideas, how well you present them in the report, how well you position your work in the related literature, how thorough are your experiments and how thoughtful are your conclusions.

Course project is typically an individual effort.

Project proposal

Progress Report

Final in-class Presentation

Final Report

For your final project write-up you must use ACM SIG Proceedings Template (available at the ACM website). Project report is at most 12 pages long, plus extra pages for references. Your report must of “publishable quality,” i.e., no typos, grammar error.

The final deadline for project report submission is 11th of December, midnight EST. This is a firm deadline. You will incur a penalty of 40% if you do not meet this deadline. These strict rules mimic conference submission process:

One pager

A one pager is a summary of the paper (assigned reading for that week). A one pager should not be more than 1 page long (12 pt font). The summary should describe what the paper is doing, its strengths and weaknesses. It should also identify possible future directions for research. One pager is marked according to the following rubric:

Reading material

You will find the following computer vision books useful.

Following books are good resources for machine learning, especially deep learning

These resources will not only help you understand the assigned papers. These resources may prove invaluable for your course projects.

Programming Resources

Here you’ll find a number of tutorials showcasing Python use in machine learning. I strongly recommend that you become comfortable with the following four Python packages/environment: