Introduction to Image Analysis

10 credits

Syllabus, Master's level, 1MD110

Education cycle
Second cycle
Main field(s) of study and in-depth level
Computer Science A1N, Image Analysis and Machine Learning A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 27 February 2020
Responsible department
Department of Information Technology

Entry requirements

120 credits including 30 credits mathematics and 30 credits computer science. Basic programming, statistics and probability theory, linear algebra, and calculus. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

After passing the course the student should be able to

  • explain basic concepts in computerized image analysis, such as discretization, image enhancement, segmentation and classification based on image features;
  • use advanced filtering methods to reduce noise and enhance edges;
  • critically evaluate a number of sophisticated segmentation methods;
  • describe and analyse digital topology and geometry in 2 and 3 dimensions;
  • apply and critically analyse classification algorithms for image interpretation;
  • apply different techniques for quality assessment of segmentation, quantitative analysis and classification;
  • compare different specific methods for image processing and image analysis;
  • use software to implement algorithms that solve simple image analysis problems;
  • analyse and independently plan all the steps needed to solve a realistic image analysis problem;
  • give examples of applications in research and industry where image analysis is used;
  • assess the possibilities and limitations of using digital image analysis for various purposes;
  • orally and in writing report and discuss problems and solutions in dialogue with different groups.


Methodology for solving image analysis problems. An overview of the basic components included in a typical image analysis system. Representation of images in a computer. Image types. Colour Theory. Sampling and interpolation. Image encoding and compression. Image enhancement and image restoration. Basic frequency analysis. Histogram operations. Point and neighbourhood operations. Segmentation and edge detection in images. Image registration and motion analysis. Computer vision. Mathematical morphology, discrete geometry and combinatorial optimization. Shape analysis and feature extraction. Classification and decision theory. Experimental design and evaluation. Examples of applications in research and industry. Opportunities and limitations of computerized image analysis.


Lectures, computer exercises, assignments, and seminars.


Written exam (5hp).

Mandatory assignments and active participation in seminars (5 hp).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

This course cannot be included in the same degree as 1TD396 Computer Assisted Image Analysis I, 1TD398 Computer Assisted Image Analysis II, and 1MD160 Computer Assisted Image Analysis.

No reading list found.