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Syllabus for

Academic year
SSY097 - Image analysis  
Syllabus adopted 2016-02-10 by Head of Programme (or corresponding)
Owner: MPBME
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Bioengineering, Electrical Engineering

Teaching language: English
Open for exchange students: Yes
Block schedule: C

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0116 Examination 4,5c Grading: TH   4,5c   23 Mar 2019 am SB_MU   10 Jun 2019 pm SB_MU   19 Aug 2019 pm SB_MU  
0216 Laboratory 1,5c Grading: UG   1,5c    
0316 Project 1,5c Grading: UG   1,5c    

In programs



Fredrik Kahl


ESS060   Image analysis SSY095   Image analysis SSY096   Image analysis


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

A basic course in Signals and Systems (or the equivalent) including the Fourier Transform, linear filter theory (impulse response, transfer function, convolution, sampling theorem). Working knowledge of probability theory.


The main aim of the course is to give a basic introduction to the algorithms and mathematical methods used in image analysis, to an extent that will allow the student to handle industrial image analysis problems. In addition the aim is to help the student develop his or her ability in problem solving, both with or without a computer.

Learning outcomes (after completion of the course the student should be able to)

Knowledge and understanding

For a passing grade the student must
- be able to explain clearly, and to independently use, basic mathematical concepts in image analysis.
- be able to describe and give an informal explanation of the mathematical theory behind some central image analysis algorithms (both deterministic and stochastic).
- have an understanding of the statistical principles used in machine learning

Competences and skills

For a passing grade the student must
- in an engineering manner be able to use computer packages to solve problems in image analysis.
- show good capability to independently identify problems which can be solved with methods from image analysis, and be able to choose an appropriate method.
- be able to independently apply basic methods in image analys 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 image analysis.


Basic image analysis tools: Filtering, morphology, scale space representations.

Extraction of image features: Blob, edge and corner detection.
Image similarity: Correlation, mutual information and the SIFT descriptor.

Image registration: Robust model fitting, RANSAC, intensity-based methods.

Basics of computer vision: camera geometry, epipolar geometry and motion estimation

Segmentation: thresholding, multi-atlas segmentation and graph-methods.

Machine learning-based methods for classification: Nearest neighbour, SVM, random forests and convolutional nets.

Shape modelling: Active shape models.

Applications: Computer-aided diagnostics (segmentation, alignment, classification), robotic vision (motion estimation, object/scene recognition) and image search.


The course consists of a number of lectures (including guest lectures given by industry and academic researchers showcasing practical applications of image analysis). In addition there are a number of exercise sessions, four laboratory sessions and one project. The project may be carried out individually or in pairs. The project involves the submission of a written report explaining the image analysis problem at hand, a motivation of the chosen theory and algorithms, results and conclusions.


Szeliski, R.: Computer Vision, Algorithms and Applications. Springer, 2010, ISBN: 9781848829343. It is possible to pass the course without owning the book, using material available through the course page.

Examination including compulsory elements

Written exam with TH grading, laboratory sessions (pass/fail), and project (pass/fail).

Published: Mon 28 Nov 2016.