Course syllabus for Spatial statistics and image analysis

The course syllabus contains changes
See changes

Course syllabus adopted 2019-02-22 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 20114
  • Open for exchange studentsYes

Credit distribution

0101 Examination 7.5 c
Grading: TH		 7.5 c
  • 02 Jun 2021 pm J
  • Contact examiner
  • 25 Aug 2021 pm J

In programmes

Examiner

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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 syllabus contains changes

  • Changes to examination:
    • 2021-12-22: Exam by department No longer exam by department by Montell
      [7,5 hec, 0101] Not given by dept
    • 2021-04-14: Exam date Exam date changed by Elisabeth Eriksson
      [32150, 53789, 3], New exam for academic_year 2020/2021, ordinal 3 (not discontinued course)
    • 2020-12-22: Examination datetime Examination datetime 2021-06-02 Afternoon added by Montell
      [7,5 hec, 0101]
  • Changes to course rounds:
    • 2021-02-03: Examinator Examinator changed from Stefan Lemurell (sj) to Aila Särkkä (aila) by Viceprefekt/adm
      [Course round 1]
    • 2021-01-29: Examinator Examinator changed from Krzysztof Podgorski (krzpod) to Stefan Lemurell (sj) by Viceprefekt
      [Course round 1]
    • 2020-12-18: Examinator Examinator changed from Mats Rudemo (rudemo) to Krzysztof Podgorski (krzpod) by Viceprefekt/adm
      [Course round 1]