Course syllabus

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EEN020 Computer vision lp2 HT22 (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 students to develop their ability to solve problems, both with and without computer, using tools derived from many different sciences, especially geometry, optimization, statistics and computer science.

Schedule

You can see the schedule and lecture room in TimeEdit.

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 (or here directly)

For pointers to relevant chapters, see Schedule.

For more in-depth reading, the book by Hartley & Zisserman 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

Passing the course

The main task in order to pass the course is to work on the assignments and the final project. Each assignment has a number of exercises falling into the following categories:

  • Mandatory theoretical exercises. You don't need to solve all of these, but obtain at least 50% of achievable points for the mandatory theoretical exercises.
  • Mandatory computer exercises. Since most of these exercises yield one or several subroutines that you need for the final project, full completion of these exercises is required.
  • Optional exercises (both theoretical and on the computer). These are only relevant for you if you want to obtain a higher grade.

The requirement to pass the course is, that for each assignment you complete the mandatory computer exercises and obtain at least 50% of points for the mandatory theoretical exercises. If there are minor errors in these mandatory exercises that prevent you from passing the assignment, you will be given one chance to correct them and resubmit within one week after getting the feedback from the TAs. You cannot resubmit your solutions to the optional exercises.

There are mandatory and optional parts in the final project!

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 max
  • Project: 10 optional points max

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.

Deadlines

You need to submit your code and reports for your assignments before the following dates in Canvas. The deadlines to hand in the solutions for the assignments are as follows:

  1. Assignment 1: Thursday, November 10, 23:59
  2. Assignment 2: Thursday, November 17, 23:59
  3. Assignment 3: Thursday, November 24, 23:59
  4. Assignment 4: Thursday, December 1, 23:59
  5. Assignment 5: Thursday, December 8, 23:59

If you submit the assignments late (but before the following Sunday 23:59), you won't have a chance to resubmit and the optional tasks won't be graded. If you submit even later, you will fail the assignment. 

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

The project description will be released on Thursday December 1, and the project deadline is Monday, January 2, 23:59. Like with the assignment, the project has core and optional parts.

Student representatives for this course

TBA

VLFeat library for Octave on Linux

The precompiled zip archive is vlfeat-0.9.21.zip.

It is compiled on Ubuntu 20.04 using clang-11 with OpenMP support. You need to install the libomp5-11 package from the Ubuntu repository. The tested Octave version is 5.2.0 and you need to install the octave-common and octave-image packages (from the Ubuntu repository).

Quick instructions:

1. Unpack the zip file (let's say in ~/software).

2. Run octave, and on the octave prompt type

addpath <VLFEATROOT>/toolbox
vl_setup

Here <VLFEATROOT> is replaced with the directory containing vlfeat, e.g. /home/<user>/software/vlfeat-0.9.21.

3. Check the version and configuration

vl_version verbose

You should be able to use the MATLAB interface of vlfeat. See also https://www.vlfeat.org/install-octave.html.

 

Course summary:

Date Details Due