Syllabus for |
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TMS016 - Spatial statistics and image analysis
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| Spatial statistik och bildanalys |
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| Syllabus adopted 2019-02-22 by Head of Programme (or corresponding) |
| Owner: MPENM |
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7,5 Credits
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| Grading: TH - Five, Four, Three, Fail |
| Education cycle: Second-cycle |
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Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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Teaching language: English
Application code: 20136
Open for exchange students: Yes
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0101 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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03 Jun 2020 pm J, |
Contact examiner
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26 Aug 2020 pm J
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In programs
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Mats Rudemo
Go to Course Homepage
Eligibility:
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
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.