Course syllabus

Image Analysis

FMAN20, 7,5 credits, A (Second Cycle)

Valid for: 2015/16
Decided by: Education Board B
Date of Decision: 2015-04-16

General Information

Elective for: BME4-sbh, BME4-br, C5, D4-bg, E4-mt, E4-bg, F4, F4-bg, F4-bm, L5-gi, Pi4-bg, Pi4-biek
Language of instruction: The course will be given in English on demand


The main aim of the course is to give a basic introduction to theory and mathematical methods used in image analysis, to an extent that will allow the student to handle industrial image processing problems. In addition the aim is to help the student develop his or her ability in problem solving, both with or without a computer. A further aim is to prepare the student for further studies in e.g. computer vision, multispectral image analysis and statistical image analysis.

Learning outcomes

Knowledge and understanding
For a passing grade the student must



Competences and skills
For a passing grade the student must




Basic mathematical concepts: Image transforms, Discrete Fourier Transform, Fast Fourier Transform.

Image enhancement: Grey level transforms, filtering.

Image restoration: Filterings, inverse methods.

Scale space theory: Continuous versus discrete theory, interpolation.

Extraction of special features: Filtering, edge and corner detection.

Segmentation: graph-methods, active contours, mathematical morphology.

Bayesian image handling: Maximum A Posterori (MAP) estimations, simulation.

Pattern recognition: Classification, SVM (Support Vector Machine), Principal Component Analysis (PCA), learning.


Machine Learning: Training, testing, generalization, hypothesis spaces.

Examination details

Grading scale: TH
Assessment: Compulsory computer exercises and assignments. Approved results on these are enough to pass the course. To get a higher grade, a written and an oral test are required.

Code: 0114. Name: Image Analysis, Theory Course.
Credits: 7,5. Grading scale: TH.
Code: 0214. Name: Image Analysis, Laboratory Work.
Credits: 0. Grading scale: UG.


Required prior knowledge: FMAF05 Systems and Transforms, or equivalent (for example FMAF10 Applied Mathematics - Linear Systems).
The number of participants is limited to: No
The course overlaps following course/s: FMA170, FMA172

Reading list

Contact and other information

Director of studies: Anders Holst,
Course coordinator: Kalle Åström,
Course homepage: