Everyone has large photo collections these days. How can you intelligently find all pictures in which your dog appears? How can you find all pictures in which you are frowning? Can we make cars smart, e.g., can the car drive you to school while you finish your last homework? How can a home robot understand the environment, e.g., switch on a tv when being told so and serve you dinner? If you take a few pictures of your living room, can you reconstruct it in 3D (which allows you to render it from any new viewpoint and thus allows you to create a "virtual tour" of your room)? Can you reconstruct it from one image alone? How can you efficiently browse your home movie collection, e.g. find all shots in which Tom Cruise is chasing a bad guy?

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. Since Kinect is popular these days, we will also try to squeeze recognition with RGB-D data into the schedule. 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.

Prerequisites: A second year course in data structures (e.g., CSC263H), first year calculus (e.g., MAT135Y), and linear algebra (e.g., MAT223H) are required. Students who have not taken CSC320H will be expected to do some extra reading (e.g., on image gradients). Matlab will extensively used in the programming excercises, so any prior exposure to it is a plus (but not a requirement).

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When emailing us, please put CSC420 in the subject line.

Information Sheet

The information sheet for the class is available here.

Programming Language(s)

You are expected to do some programming assignments for the class. You can code in either Matlab, Python or C. However, in class we will provide the examples and functions in Matlab. Note also that most Computer Vision code online is in Matlab so it's useful to learn it. Knowing C is only a plus since you can interface your C code to Matlab via "mex".

Please make sure you have access to MATLAB with the Image Processing Toolbox installed.


This class uses piazza. On this webpage, we will post announcements and assignments. The students will also be able to post questions and discussions in a forum style manner, either to their instructors or to their peers.

Please sign up here in the beginning of class.

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We will not directly follow any textbook, however, we will require some reading in the textbook below. Additional readings and material will be posted in the schedule table as well as the resources section.

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Each student is expected to complete four assignments which will be in the form of problem sets and programming problems, and complete a project.


Assignments will be given every two weeks. They will consist of problem sets and programming problems with the goal of deepening your understanding of the material covered in class. All solutions and programming should be done individually. There will be four assignments altogether, each worth 15% of the final grade.

Deadline: The solutions to the assignments should be submitted by 11.59pm on the date they are due. Anything from 1 minute late to 24 hours will count as one late day.

Lateness policy: Each student will be given a total of 3 free late days. This means that you can hand in three of your assignments one day late, or one assignment three days late. It is up to you to make a good planning of your work. After you have used your 3 day budget, your late assignments will not be accepted.

Plagiarism: We take plagiarism very seriously. Everything you hand in to be marked, namely assignments and projects, must represent your own work. Read How not to plagiarize.


Each student will be given a topic for the project. You will be able to choose from a list of projects, or propose your own project which will need to be discussed and approved by your instructor. You will need to hand in a report which will count 25% of your grade. Each student will also need to present and be capable to defend his/her work. The presentation will count 15% of the grade.

The final grade will be computed as follows:

(4 assignments, each worth 15%)
(report: 25%, presentation: 15%)

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The course will cover image formation, feature representation and detection, object and scene recognition and learning, multi-view geometry and video processing. Since Kinect is popular these days, we will also try to squeeze recognition with RGB-D data into the schedule.

Image Processing
Linear filters
Edge detection
Features and matching
Keypoint detection
Local descriptors
Low-level and Mid-level grouping
Region proposals
Hough voting
Face detection and recognition
Object recognition
Object detection
Part-based models
Image labeling
Image formation
Multi-view reconstruction
Video processing
Action recognition
close Tentative Schedule

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DateTopicReading SlidesAdditional materialAssignments
Jan 3Course Introduction lecture1.pdf
Image Processing
Jan 3Linear FiltersSzeliski book, Ch 3.2lecture2.pdf
tutorial1.pdf (by Hang Chu)
code: finding Waldo, smoothing, convolution
Jan 9Edge DetectionSzeliski book, Ch 4.2lecture3.pdf
code: edges with Gaussian derivativesAssignment 1: due Jan 22, 11.59pm, 2017. Submit on MarkUs
Jan 16Image PyramidsSzeliski book, Ch 3.5lecture5.pdf
tutorial2.zip (by Hang Chu)
Features and Matching
Jan 23Keypoint Detection: Harris Corner DetectorSzeliski book, Ch 4.1.1
pages:   209-215
given by Makarand Tapaswi
Jan 30Keypoint Detection: Scale Invariant KeypointsSzeliski book, Ch 4.1.1
pages:   216-222
Feb 6Local Descriptors: SIFT,
Szeliski book, Ch 4.1.2
Lowe's SIFT paper
tutorial3.m (by Hang Chu)
code: SIFT codeAssignment 2: due Feb 20, 11.59pm, 2017
Feb 13Robust Matching, HomographiesSzeliski book, Ch 6.1lecture9.pdfcode: Soccer and screen homography
Feb 13Camera ModelsSzeliski, 2.1.5, pp. 46-54
Zisserman & Hartley, 153-158
(hi-res version)
Feb 27Homography revisitedlecture11.pdf
(hi-res version)
Assignment 3: due March 13, 11.59pm, 2017
March 6Stereo: Parallel Optics lecture12.pdf
(hi-res version)
code: Yamaguchi et al.
March 6Stereo: General CaseSzeliski book, Ch. 11.1
Zisserman & Hartley, 239-261
(hi-res version)
Projects: due April 5, 11.59pm, 2017
March 13Recognition: OverviewGrauman & Leibe, Visual Object Recognitionlecture14.pdf
(hi-res version)
March 20Fast RetrievalSivic & Zisserman, Video Googlelecture16.pdf
(hi-res version)
Assignment 4: due March 25, 11.59pm, 2017
March 20Implicit Shape ModelB. Leibe et al., Robust Object Detection with Interleaved Categorization and Segmentationlecture17.pdf
(hi-res version)
March 27The HOG DetectorHOG paperlecture18.pdf
(hi-res version)
Jialiang Wang's Tutorial on classification (HOG+SVM)
March 27Deformable Part-based ModelDPM paperlecture19.pdf
(hi-res version)

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We will be posting the best solutions here.

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