Course syllabus adopted 2026-02-20 by Head of Programme (or corresponding).
Overview
- Swedish nameBildanalys
- CodeSSY099
- Credits7.5 Credits
- OwnerMPMED
- Education cycleSecond-cycle
- Main field of studyBioengineering, Electrical Engineering, Biomedical engineering
- DepartmentELECTRICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 41119
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0126 Project 3.5 c Grading: TH | 3.5 c | ||||||
| 0226 Laboratory 4 c Grading: UG | 4 c |
In programmes
- MPDSC - Data Science and AI, Year 1 (compulsory elective)
- MPICT - Information and Communication Technology, Year 1 (compulsory elective)
- MPMED - Biomedical Engineering, MSc Progr, Year 1 (compulsory elective)
- MPSYS - Systems, Control and Mechatronics, Year 1 (elective)
- TKAUT - Automation and Mechatronics Engineering, Year 3 (elective)
Examiner
- Jennifer Alvén
- Assistant Professor, Signal Processing and Biomedical Engineering, Electrical Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements
Course specific prerequisites
Basic knowledge of signals and systems, including filtering and convolution.Basic knowledge of probability theory and statistics, including random variables, distributions, expectation, and variance.
Basic knowledge of linear algebra and multivariable calculus, including vectors and matrices, partial derivatives, gradients, and the chain rule.
Basic programming skills in Python or an equivalent language.
Aim
The aim of the course is to provide the student with a solid foundation in mathematical models, methods, and algorithms for image analysis. The course covers both learning-based and geometric approaches and aims to enable the student to analyze image analysis tasks, implement and evaluate appropriate methods, and critically compare and justify solutions. After completing the course, the student should be able to apply image analysis methods to problems relevant to industrial computer vision, autonomous systems, medical image analysis, and research.Learning outcomes (after completion of the course the student should be able to)
Knowledge and understandingFor a passing grade the student must be able to:
-explain fundamental mathematical concepts and models used in image analysis, including image representations, similarity measures and metrics, and image processing operations.
-explain the mathematical theory and principles behind some central image analysis algorithms (both learning-based and geometric methods).
-explain the role of statistical principles used in image analysis and machine learning.
Competences and skills
For a passing grade the student must be able to:
-independently implement and evaluate image analysis methods using appropriate software tools and libraries, primarily in Python.
-analyze an image analysis task (e.g. classification, segmentation, or detection) and select appropriate image analysis methods based on problem formulation, assumptions, data characteristics and performance.
-apply appropriate image analysis methods to problems relevant to industrial computer vision, autonomous systems, medical image analysis, or research.
-evaluate and compare solutions to an image analysis problem, and justify conclusions using correct terminology and logical structure.
Content
Digital image representations, image similarity, and coordinate representations.Basic image processing, including filtering and feature extraction.
Different types of image analysis tasks, such as classification, segmentation, and detection, as well as principles for their evaluation.
Machine learning for image analysis, including supervised learning, deep learning, and convolutional neural networks.
Modern deep learning architectures and learning paradigms for image analysis.
Geometric models for image analysis, including robust model fitting and image registration.
Camera models, multi-view geometry, and methods for motion estimation and three-dimensional structure.
Generative image models.
Applications in industrial computer vision, autonomous systems, and medical image analysis.
Organisation
The course consists of a number of lectures (including guest lectures given by industry and / or academic researchers showcasing practical applications of image analysis). In addition there are a number of exercise sessions, four laboratory sessions and one project. The laboratory sessions may be carried out individually or in groups, but the project needs to be carried out individually. 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.Literature
Optional: Szeliski, R.: Computer Vision, Algorithms and Applications. Springer, 2010, ISBN: 9781848829343.
optional: Goodfellow, I. and Bengio, Y. and Courville, Deep Learning. MIT Press, 2016, ISBN: 9780262035613.
It is possible to pass the course without owning the books, using material available through the course page. Both books are available online for free (as pre-prints).
Examination including compulsory elements
The course is examined through compulsory laboratory assignments, an individual project, and tests conducted during the course.The laboratory assignments aim to train practical application of image analysis methods and are assessed on a pass/fail basis. During the course, tests are conducted to assess the students understanding of the methods and concepts covered in the laboratory assignments. All laboratory assignments and quizzes must be passed in order for the student to obtain a passing grade in the course.
The project is carried out individually and includes analysis of an image analysis task, selection and implementation of appropriate methods, as well as evaluation and comparison of solutions. The project is presented in a written report and is grading for the course. In connection with the project, an oral examination is conducted. The oral examination is optional but required in order to obtain a grade of 4 or 5 in the course.
There is no written examination in the course.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.