Course Description

This class is an introduction to fundamental concepts in image understanding, the subdiscipline of artificial intelligence that tries to make the computers “see”. It will survey a variety of interesting vision problems and techniques. Specifically, the course will cover image formation, features, object and scene recognition and learning, multi-view geometry and video processing. The goal of the class will be to grasp a number of computer vision problems and understand basic approaches to tackle them for real-world applications.

Instructor

Course Logistics

Lectures: Monday 2:00pm-4:00pm (Galbraith Building 248). Lectures will be recorded and made available on Quercus.

Tutorials: Wednesday 3:00pm-4:00pm (Galbraith Building 119).

Instructor office hours: Monday 4:00-5:00pm (BA7228).

TA office hours: Thursdays 10:00am-11:00am (BA2270).

Forum: Course announcements and general information will be posted on the course forum at Ed Discussion. Any questions can also be discussed on the course forum, though do not expect an immediate response from the TAs. Also, do not expect answers during weekends or at the last minute before assignments are due. For any emergency inquiries that cannot be handled on the forum via a private post, email the staff listserv: csc420-2023-01@cs.toronto.edu.

Attendance: While lectures will be recorded, in-person attendance is expected as the recording will not capture all presented material (e.g., handwritten annotations, chalkboard diagrams, etc.).

Quercus: Assignment materials, tutorial code, and grades will be posted on Quercus.

Textbook: Readings are primarily assigned from Richard Szeliski's online textbook, which is online and freely available (2010 version). Some topics will have specific additional assigned reading materials as indicated in the course schedule.

Assignments: All assignments must be submitted on MarkUs. You will be automatically added to MarkUs if you're taking the course. Please do not email the staff if you are not yet added at the beginning of the term; it may take a week or two to be added.

Remark requests must be submitted within 1 week of graded assignments being returned. Late remark requests will not be accepted.

Lateness: Each student will be given a total of 3 late days without penalty. For example, you can hand in three of the assignments one day late, or one assignment three days late. These late days are to be used at your discretion. If you run out of late days, any subsequent assignments that are submitted late will not be accepted.

Extensions will only be given with university approval (i.e., a formal signed letter of accommodation from the university accessibility services office) up to a maximum of 7 days.

Grading

Assignments (64%)

There will be 4 assignments, posted every two weeks, starting with the second week. Assignments will consist of problem sets and programming problems with the goal of deepening your understanding of the material covered in class.

Collaboration Policy: For each assignment, you are allowed to work together with one other student in class. However, you are still expected to write the solutions, code, and writeup in your own words; i.e. no copying. If you choose to work together with another student, you must write the following on the first line of your writeup (after your own name and before answering any questions): “In solving the questions in this assignment, I worked together with my classmate [name & student number]. I confirm that I have written the solutions/code/report in my own words”.

Submitting plagiarized solutions is an academic offense and can have severe penalties.

Final Exam (35%)

Details of the final exam will be announced later in the course.

Ethics Module (1%)

Completion of two surveys based on a course ethics module of the course will be worth 0.5% each.

Schedule and Syllabus

Week Date Description Material Readings Event Deadline
Week 1 Mon
9/1
Lecture 1: Introduction & Linear filters
[slides] Szeliski 3.2
(optional) Brain mechanisms of early vision
(optional) Early vision
Assignment 1 out on Quercus
Wed
11/1
Tutorial 1 [code]
Thu
12/1
TA Office Hours
Week 2 Mon
16/1
Lecture 2: Edges
[slides] Szeliski 4.2
(optional) Fourier Transform
(optional) Computer color is broken
Wed
18/1
Tutorial 2 [code]
Thu
19/1
TA Office Hours
Week 3 Mon
23/1
Lecture 3: Image pyramids
[slides] Szeliski 3.5
(optional) Pyramid methods
Wed
25/1
Tutorial 3 [code]
Thu
26/1
TA Office Hours
Fri
27/1
Assignment 1 due at 11:59pm
Week 4 Mon
30/1
Lecture 4: Deep learning
[slides] What is a neural network?
Gradient descent
Backpropagation
Backpropagation calculus
(optional) Notes on backpropagation
Assignment 2 out on Quercus
Wed
1/2
Tutorial 4 [code]
Thu
2/2
TA Office Hours
Week 5 Mon
6/2
Lecture 5: Embedded ethics module [slides] CelebA-HQ Samples
Wed
8/2
Tutorial 5 [code]
Thu
9/2
TA Office Hours
Week 6 Mon
13/2
Lecture 6: Corner detection & optical flow
[slides] Szeliski 4.1.1
Wed
15/2
Tutorial 6 [code]
Thu
16/2
TA Office Hours
Fri
17/2
Assignment 2 due at 11:59pm
Week 7 Mon
20/2
Reading Week (No Lecture)
Assignment 3 out on Quercus
Week 8 Mon
27/2
Lecture 7: Scale-invariant keypoints & SIFT
[slides] Szeliski 4.1.1, 4.1.2
(optional) SIFT paper
(optional) Local features
(optional) SURF paper
(optional) SuperGlue
(optional) LoFTR
Wed
1/3
Tutorial 7 [code]
Thu
2/3
TA Office Hours
Week 9 Mon
6/3
Lecture 8: Affine transformation & RANSAC [slides]
Wed
8/3
Tutorial 8 [code]
Thu
9/3
TA Office Hours
Fri
10/3
Assignment 3 due at 11:59pm
Week 10 Mon
13/3
Lecture 9: Camera models & homography I
[slides] Szeliski 6.1, 2.1.5
(optional) Moore-Penroose Inverse and Least Squares
(optional) Recognising Panoramas
Assignment 4 out on Quercus
Wed
15/3
Tutorial 9 [code]
Thu
16/3
TA Office Hours
Week 11 Mon
20/3
Lecture 10: Homography II [slides]
Wed
22/3
Tutorial 10 [code] (optional) Face tracking
Thu
23/3
TA Office Hours
Week 12 Mon
27/3
Lecture 11: Stereo [slides] Szeliski 11.1
(optional) Stereopsis
(optional) Short notes on stereopsis
(optional) 8-point algorithm
(optional) Singular value decomposition
Thu
30/3
TA Office Hours
Fri
31/3
Assignment 4 due at 11:59pm
Week 13 Mon
3/4
Lecture 12: Object detection [slides] (optional) K-means demo
Thu
6/4
TA Office Hours
TBD Final Exam

Miscellanea

Installing Python

We will only support Python 3.7 and recommend that you install it using Anaconda or Miniconda (see installation instructions here).

Acknowledgements

This course is adapted from previous offerings by Sanja Fidler, Ahmed Ashraf, and Babak Taati.