Skip to content
Main menu hidden.

Project course in Machine Vision, 7.5 Credits

Swedish name: Projektkurs i datorseende

This syllabus is valid: 2024-01-01 and until further notice

Course code: 5DV190

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Computing Science: Second cycle, has second-cycle course/s as entry requirements
Computational Science and Engineering: Second cycle, has second-cycle course/s as entry requirements

Grading scale: TH teknisk betygsskala

Responsible department: Department of Computing Science

Established by: Faculty Board of Science and Technology, 2018-03-19

Revised by: Faculty Board of Science and Technology, 2023-06-19


The course is about an application within one or more of the subjects Image Analysis, 3d Reconstruction, and Pattern Recognition. The relevant topics and theory about a project management model, e.g. Scrum, are introduced initially, followed by a larger software development project. The design of the project varies from year to year. Examples of possible projects are:

  • Design an application that takes a set of images of a living room, builds a 3D model of it and visualizes it together with the IKEA bookcase "Billy" (or another piece of furniture).
  • Design an application that given a number of scanned itemized phone bills calculates the phone subscription plan that would be the cheapest.
  • Design an application that given a point cloud acquired by a laser scanner in a sparse forest, identifies trees and calculates their diameter at breast height.
  • Develop a method that given inputs such as digital images and/or 3D point clouds from a forest, detects trees and classifies tree species.
  • Identify and localize fruits in images using classifier fusion.
  • Analyze 3D images from a Kinect-camera and detect people, trees, shrubs, and stones.

Expected learning outcomes

Knowledge and understanding
After completing the course, the student should be able to:

  • (FSR 1) explain central concepts within image analysis, 3D reconstruction, and/or pattern recognition,
  • (FSR 2) account for the priciples of the used project management model, e.g. Scrum.

Competence and skills
After completing the course, the student should be able to:

  • (FSR 3) demonstrate an ability to work in projects in groups of at least 4 people, including working in non-self-selected groups,
  • (FSR 4) analyze a problem within one or more of the subjects image analysis, 3D reconstruction, and pattern recognition,
  • (FSR 5) identify ambiguities in the given problem specification and to propose clarifications,
  • (FSR 6) use a version control system for source code and other documentation.

Judgement and approach
After completing the course, the student should be able to:

  • (FSR 7) evaluate different proposed solutions for problems in one or more of the subjects image analysis, 3D reconstruction, and pattern recognition,
  • (FSR 8) reflect on their own effort in a project and assess the quality of the result of the group.

Required Knowledge

At least 90 ECTS, including 60 ECTS Computing Science, or 120 ECTS within a study programme. At least 7.5 ECTS programming; 7.5 ECTS linear algebra; 7.5 ECTS differential calculus; and 7.5 statistics. Proficiency in English equivalent to the level required for basic eligibility for higher studies.

Form of instruction

The course begins with an introduction in the form of lectures to the relevant topics and the project managament model. Then follows a larger programming project that is central to the course. The aim is to get experience of working in a development team to generate a working prototype from a vague problem specification. A further goal is to individually and in groups gather knowledge necessary for the task. The work is primarily organized under an agile development model, such as Scrum. The work includes work in small and large groups and in-depth studies.

Examination modes

The examination on the course consists of a written account of the student's effort in the project, mainly as a time log (FSR 3, 8), and a written final report in the form of a home exam (FSR 1-2, 4-8). Since the practical work in a group is central to the course, the majority of the attendance during the practical work is mandatory.

On the course, the grades given are Fail (U), Pass (3) or Pass with Mark (4), or Pass with Distinction (5).

The course grade is an overall assessment of the two examinations parts.

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

In an exam, this course may not be included, in whole or in part, simultaneously with another course of similar content. If
in doubt, consult the student counselors at the Department of Computer Science and / or program director of the study

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.


Valid from: 2024 week 1

Väljs i samråd med kursansvarig utifrån individuell frågeställning.
The litterature is chosen, together with the teacher responsible for the course, to best fit to the chosed research question. :