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Institutionernas kurser för doktorander

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Kursplan för

Läsår
TMS016 - Statistisk bildbehandling
 
Kursplanen fastställd 2012-02-22 av programansvarig (eller motsvarande)
Ägare: MPENM
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Matematik
Institution: 11 - MATEMATISKA VETENSKAPER


Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: X

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0101 Tentamen 7,5hp Betygskala: TH   7,5hp   29 Maj 2013 fm V,  22 Aug 2013 fm V

I program

MPAPP APPLIED PHYSICS, MSC PROGR, Årskurs 1 (valbar)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Årskurs 2 (valbar)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Årskurs 2 (valbar)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Årskurs 1 (valbar)

Examinator:

Professor  Mats Rudemo



  Gå till kurshemsida

Behörighet:

För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

The student is supposed to have completed a basic course in mathematical statistics worth of 7.5 credit points.

Syfte

The aim of the course is to provide a basic knowledge of how to use probabilistic and statistical methods for image analysis.

Lärandemål (efter fullgjord kurs ska studenten kunna)

- have experience with image acquisition and basic image processing,
- be able to understand how to use statistical image analysis
techniques including methods for pattern recognition
- have competence of applying statistical modeling to image analysis, including the use of models for point patterns and spatial correlation.

Innehåll

Digital image processing and analysis of information in images are methods that become increasingly important in many technical and scientific fields, including almost all biological sciences.
Methods for acquiring, showing, filtering and segmentation of images are briefly covered in the first part of the course, including methods for performing quantitative measurements in images.
Core subjects in the course are pattern recognition and spatial
statistics applied to images. In pattern recognition we study methods for discrimination between classes of objects characterized by suitably chosen features. Spatial statistical models are used for describing point patterns, spatial correlation and the shape and structure of objects in two and three dimensions.
Examples are taken from remote sensing, microscopy, photography, medical imaging and fingerprint analysis. In the course special interest will be devoted to applications in bioinformatics, including analysis of images of microarrays for comparing DNA expression levels and images of two-dimensional electrophoresis gels for studying proteins.

Organisation

Lectures.
Practical computer work is included, typically using MATLAB. An
important part of the course is to carry through a project in a small
group, presenting the results at a seminar and writing a project
report.

Litteratur

To be decided

Examination

The assessment is based both on a written final examination and the project report.


Sidansvarig Publicerad: må 13 jul 2020.