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Syllabus for

Academic year
TMS016 - Spatial statistics and image analysis
Spatial statistik och bildanalys
 
Syllabus adopted 2019-02-22 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Application code: 20114
Open for exchange students: Yes

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0101 Examination 7,5c Grading: TH   7,5c    

In programs

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (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


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.


Published: Mon 28 Nov 2016.