Course syllabus


EEN020 Computer vision lp2 HT20 (7.5 hp)

Course is offered by the department of Electrical Engineering


Contact details

Course purpose

The course aims to provide an overview of theory and practical useful methods in computer vision, with applications such as seeing systems, non-destructive measurements and augmented reality. The aim is also to enable the student to develop his / her ability to solve problems, both with and without computer, using tools derived from many different sciences, especially geometry, optimization, statistics and computer science.


You can  see the schedule and lecture room in TimeEdit.

The course lectures and the TA (exercise) sessions will be purely online via Zoom. During the TA sessions, you will work on your assignments, while having access to help from a teaching assistant.
The Zoom link for the main lectures is (password: 127914).
The Zoom link for the TA sessions can be found in any of the assignment pages, and is fixed for all sessions.

For best experience with the TA sessions, please use a recent desktop (or mobile) version of Zoom (>= 5.3.0, not the web version).
Desktop version:
Android version:
iOS version:

Course literature

The necessary course material will be provided during the course. This includes lecture notes, assignments and research articles.
If you like to read more about computer vision, you can use Szeliski's book which is available online.

  • Richard Szeliski, Computer Vision: Algorithms and Applications, available at Cremona or as a free pdf.

For pointers to relevant chapters, see Schedule.

For more in-depth reading, the Hartley-Zisserman book also known as the Bible is recommended.

  • Richard Hartley and Andrew Zisserman, Multiple View Geometry, Cambridge University Press, 2004.

Learning objectives and syllabus

Learning objectives:

Knowledge and understanding
For a passing grade the student must:

  • be able to clearly explain and use basic concepts in computer vision, in particular regarding projective geometry, camera modelling, stereo vision, recognition, and structure and motion problems.
  • be able to describe and give an informal explanation of the mathematical theory behind some central algorithms in computer vision (the least squares method and Newton based optimization).

Competence and skills
For a passing grade the student must:

  • in an engineering manner be able to use computer packages to independently solve problems in computer vision.
  • be able to show good ability to independently identify problems which can be solved with methods from computer vision, and be able to choose an appropriate method.
  • be able to independently apply basic methods in computer vision to problems which are relevant in industrial applications or research.
  • with proper terminology, in a well-structured way and with clear logic, be able to explain the solution to a problem in computer vision.

Link to the syllabus on Studieportalen.

Study plan


You need to submit your code and reports for your assignments before the following dates in Canvas. If there are minor errors on the mandatory exercises, you will be given a chance to correct them later. If you submit solutions to optional exercises, you will be given feedback, but resubmissions will not increase your optional points score.

  1. Assignment 1: Monday, November 9, 23:59
  2. Assignment 2: Thursday, November 19, 23:59
  3. Assignment 3: Thursday, November 26, 23:59
  4. Assignment 4: Thursday, December 3, 23:59
  5. Assignment 5: Thursday, December 10, 23:59

The assignments will be published on the first five Mondays of the course.

The project will be released on Thursday December 10, and the project deadline is Wednesday, January 6, 23:59. When you have decided which project to do, please mark this on this page by joining the Canvas group corresponding to your choice of project. This is helpful information for us to plan the grading.

Final deadline of resubmissions: Friday, January 8. Note that if you are not approved on all assignments by January 8, 2021, you will fail the course. We recommend that you do all revisions before Christmas, since correcting assignments will be done at a slower pace after that.

Optional points and higher grades

There are optional parts in each assignments, which if you choose to complete them may give you optional points. For the higher grades, sufficiently many optional points are needed. The points are distributed as follows:

  • Assignments: 5 x 2 = 10 optional points
  • Project: 4 optional points

You need at least 50% of optional point to obtain grade 4 and to be eligible for the oral exam. The oral exam will take place in the exam week for LP2 in January. The exact details of the oral exams depend on the Covid regularations that are in effect in Januray.

Student representatives for this course

Therese Gardell

André Idoffsson

Course summary:

Date Details Due