Course syllabus adopted 2021-02-26 by Head of Programme (or corresponding).
Overview
- Swedish nameSpatial statistik och bildanalys
- CodeTMS016
- Credits7.5 Credits
- OwnerMPENM
- Education cycleSecond-cycle
- Main field of studyMathematics
- DepartmentMATHEMATICAL SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 20122
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0101 Examination 7.5 c Grading: TH | 0 c | 0 c | 0 c | 7.5 c | 0 c | 0 c |
|
In programmes
- MPBME - BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
- MPCAS - COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
- MPENM - ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
- MPICT - INFORMATION AND COMMUNICATION TECHNOLOGY, MSC PROGR, Year 1 (compulsory elective)
Examiner
Ottmar Cronie- Senior Lecturer, Applied Mathematics and Statistics, Mathematical Sciences
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 above.
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 above.
Course specific prerequisites
One basic course in mathematical statistics as well as MVE170 or a similar course on stochastic processes.Aim
The aim of the course is to provide basic knowledge of models and methods with practical applications in spatial statistics and image analysis.Learning outcomes (after completion of the course the student should be able to)
- perform basic image processing, including filtering and noise reduction.
- Identify and describe stochastic models and methods for problems in spatial statistics and image analysis.
- Implement computer programs for solving statistical problems in image analysis with a given method.
- Report motivations, approaches and conclusions when solving a given statistical problem, both in writing and orally.
- Suggest and analyze stochastic models for problems in spatial statistics and image analysis.
Content
Basic methods of filtering and pattern recognition in images. Statistical methods for classification and reconstruction. Stochastic fields, Gaussian fields, Markov fields, Gaussian Markov random fields, and point processes. Covariance functions, kriging, and simulation methods for stochastic inference. Applications to climate, environmental statistics, remote sensing, microscopy, photography, and medical imaging.
Organisation
Lectures and computer exercises where MATLAB or R is used. An important part of the course is project work that is presented in a project report and at a seminar.
Literature
Listed on the course homepage no later than eight weeks before the course starts.
Examination including compulsory elements
The assessment is based on a written exam and project work.
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 on educational support due to disability.
The course syllabus contains changes
- Changes to course rounds:
- 2023-10-04: Examinator Examinator changed from Aila Särkkä (aila) to Ottmar Cronie (ottmar) by Viceprefekt
[Course round 1]
- 2023-10-04: Examinator Examinator changed from Aila Särkkä (aila) to Ottmar Cronie (ottmar) by Viceprefekt