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DD2423 Image Analysis and Computer Vision 7.5 credits

A course in computer science focusing on basic theory, models, and methods for computer vision, image analysis and image processing.

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Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Autumn 2024 CVHT23 programme students

Application code


Headings with content from the Course syllabus DD2423 (Autumn 2024–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Overview about aims and methods for image analysis, image processing and computer vision. Orientation about biological seeing and visual perception. Properties of the perspective image formation.

Basic image analysis: signal theoretical methods, filtering, image enhancement, image reconstruction, segmentation, classification, representation.

Basic computer vision: multiscale representation, detection of edges and other distinctive features. Stereo and multi-camera systems. Object recognition, morphology.

Intended learning outcomes

After completing the course with a passing grade the student should be able to:
• identify basic concepts, terminology, models and methods in computer vision and image processing
• develop and evaluate a number of basic methods in computer vision and image processing systematically
• choose and apply methods for processing of image data related to image filtrering, image enhancement, segmentation, classification and representation,
• account for basic methods in computer vision as multiscale representation, detection of edges and other distinctive features, stereo, movement and object recognition to
• later as a working professional be able to decide how basic possibilities and limitations influence the choice of methods in image processing and computer vision for specific applications
• independently be able to implement, analyse and evaluate simple methods for computer vision and image processing
• be able to read and apply professional literature in the area.

Literature and preparations

Specific prerequisites

  • Knowledge in Calculus in One Variable, 7,5 credits, equivalent to completed course SF1625/SF1673.
  • Knowledge in linear algebra, 7,5 credits, equivalent to completed course SF1624/SF1672/SF1684.
  • Knowledge in Probability Theory and Statistics, 6 credits, equivalent to completed course  SF1910-SF1924/SF1935.
  • Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.

Recommended prerequisites

The courses in the basic block on mathematics, computer science and numerical analysis on the D-, E- or F-programme. One more course on signal processing and/or numerical analysis can be recommended. We recommend the students to read the course during the fourth year because it uses prerequisites from a relative wide spectrum of applied mathematics and computer science.


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • LAB1 - Laboratory Work, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Written exam, 3.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

TEN1 is conducted as a written exam.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

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Mårten Björkman, e-post:

Supplementary information

The course has replaced DD2422 Image Analysis and Computer Vision.

In this course, the EECS code of honor applies, see: