News
Mar 27, 2025
Project presentations will take place on Wednesday, April 2 and
Friday, April 4. Each presentation is 3 minutes long. Project
presentation schedule is
here.
Mar 23, 2025
We are getting to the end of term. No new labs. The focus now is to complete the course projects.
Mar 23, 2025
Student feedback instructions are sent out via Canvas. Please take a moment to complete course feedback.
Mar 19, 2025
This Friday midterm 2 will take place during the lecture. Midterm 2 is cumulative.
Mar 19, 2025
Exercise are now uploaded.
Mar 9, 2025
Week of Mar 10 will include guest lectures.
Mar 7, 2025
Exercise are now uploaded.
Mar 6, 2025
Lecture notes and exercise are now uploaded.
Feb 28, 2025
Lecture notes and exercise are now uploaded.
Feb 22, 2025
Labs 4 and 5 are now available on Canvas.
Feb 19, 2025
Midterm 1 marks are now available on Canvas.
Feb 16, 2025
No classes during reading week. Regular programming will resume on Monday, Feb 24.
Feb 16, 2025
Feb 14 in-class notes are now uploaded.
Feb 12, 2025
Feb 12 in-class notes are now uploaded.
Feb 3, 2025
First midterm will take place on Friday,
February 7. The midterm will start at 5:10. The duration is 70 minutes.
The midterm will cover everything that we will have covered till
Wednesday, February 5. It is a paper based midterms. No aids are
allowed. It is okay, however, to bring a basic calculator, in case you
need it.
Feb 1, 2025
We covered Fourier analysis on Friday, January 30.
Jan 30, 2025
Jan 29 in-class notes are now uploaded.
Jan 23, 2025
Jan 22 in-class notes are now uploaded.
Jan 18, 2025
Lab 2 on detecting stop signs is now available on Canvas.
Jan 16, 2025
Jan 15 in-class notes are now uploaded.
Jan 14, 2025
Find data folder for labs
here.
Jan 8, 2025
Please see below for the link to the recommended textbook.
Jan 8, 2025
Labs start the week of Jan 13.
Jan 7, 2025
First day of classes, Wednesday, Jan 8.
Jan 1, 2025
Website is now online.
Description
This course introduces students to computer vision – the science and
technology to make computers “see.” The goal of computer vision is to
develop computational machinery to extract useful information from
images and videos. The course will study various steps of the overall
image analysis pipeline. Topics covered will include: image formation,
image representation, segmentation, feature extraction, motion analysis,
object detection, camera calibration, and 3D visual reconstruction. A
secondary focus of this course will be computer vision applications,
which rely heavily upon the fundamental theory and techniques covered in
this course.
Grading
- Class participation and exercises 10%
- Lab participation and completion 15%
- Midterms 60%
- Project 15%
A student must get 50% in the midterm examinations to pass this
course.
Important dates
- Midterm 1, Friday, February 7, in class.
- Study break during the week of February 17.
- Midterm 2, Friday, March 21, in class.
- Project selection by March 3
You may loose up to 10% of the course project grade if project
selection isn’t finalized by Mar 4. You may loose up to an additional
20% of the course project grade if the project selection isn’t finalized
by Mar 11. If the project isn’t selected by Mar 11, you’ll be asked to
provide a written explanation for the delay.
- Project topics presentations, March 7
- Project report due by April 6, 11:59 pm
Ontario Tech University’s academic calendar that lists important
dates (and deadlines) is available at here.
Midterm preparations
Keywords
Find the list of topics that we have
covered in this course. The hope remains that this list will help you
focus your attention on topics that are important to score well on the
midterm.
Quizzes
You can find the quizzes and sample questions here.
Course calendar
Reading assignments are from the course textbook
Computer Vision: Algorithms and Applications, 2nd Edition by
Richard Szelski.
The book is available to download in PDF format at https://szeliski.org/Book/download.php.
Or you can purchase a hard copy from your favorite bookstore.
Week 1
Topics
- Introduction
- Image representation
Notes
Lab
Week 2
Topics
Notes
Lectures
Lab
Week 3
Topics
- Linear filtering
- Template matching
Notes
Lectures
Lab
Week 4
Topics
- Image pyramids
- Fourier analysis
Notes
Lectures
Lab
Week 5
Topics
Notes
Lectures
Lab
Week 6
Topics
Notes
Lectures
Lab
Reading week break
Week 7
Notes
Lab
Week 8
Notes
Lab
Week 9
Notes
Lab
Week 10
- Midterm 2 (cumulative) on Mar 21
Notes
Week 11
Notes
Lab
- Lab continues from previous week.
Week 12
Course project (Slides)
The course project is an independent exploration of a specific
problem within the context of this course. The topic of the project will
be decided 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.
Teams of up to two students are allowed.
Final report and a
three-minutes video
For your final project write-up you must use the VCLab course project
template available at Overleaf.
Project report is expected to be between 4 to 8 pages.
Additionally, you may submit a 3 minutes video for your project.
Resources
Textbook
We will use the following textbook for this course.
Previously, we have used the following textbook for this course, and
assigning reading assignments refer t this textbook. I plan to update
the assigned readings as we move along.
Students are encouraged to take their own notes during lectures.
Other readings and notes
You may find the following notes and books useful.
Programming resources
We will primarily use Python +
OpenCV in this course. I recommend that you install Anaconda Python
Distribution, which comes prepackaged with all the necessary packages.
The following Python packages/environment are highly relevant for this
course:
Check out the development
with python for some ideas about how best to use Python for software
development.