TMS016/MSA301 Spatial statistics and image analysis

This page contains the program of the course: lectures and computer labs. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.

Program

The schedule of the course is in TimeEdit.

The course has two lectures and two computer exercises each week. Details for these are given in the schedule below, which will be updated during the course. For the lectures, the chapters covered in the books are listed, where LN denotes the lecture notes and HS denotes the Handbook of spatial statistics and EL The Elements of Statistical Learning.
The original plan was  that  lectures would be in room MVF26 and the computer exercises  in room MVF25.  However, due to the Corona virus all teaching is now planned to be performed remotely.  More about that later.

Lectures and ExercisesLink

The information below  will be updated and extended on an ongoing basis, currently  the information on Lecture 1-11 and Computer exercises 1-11 are up-to-date

Time Lecture Computer exercise
Week 1/13 Monday
10:00-11:45
L1

Introduction L1-2020, LN pages 1-22, see also pages 1-8 of Glasbey and Horgan 1995, Chapter 5, and 

L1-2019

 
  Monday
13:15-15:00
E1   Basic image processing
  Wednesday
10:00-11:45
L2 Spatial random processes L2-2020, LN pages 69-78, see also L2-2019.  
  Wednesday
13:15-15:00
E2   Gaussian fields
Week 2/14 Monday
10:00-11:45
L3

OLS, GLS and ML estimation L3-2020, LN pages 78-85, see also  L3-2019 .

 
  Monday
13:15-15:00
E3   Estimation and kriging
  Wednesday
10:00-11:45
L4

Pattern recognition L4-2020, LN pages 22-38, see also  L4-2019

 
  Wednesday
13:15-15:00
E4   Continue working on exercise 3
Week 3/17 Monday
10:00-11:45
L5

Pattern recognition, cont. MAmasterth, L5-2020, LN pages 38-52, see also  L5-2019

 
  Monday
13:15-15:00
E5   Image reconstruction using GMRFs
  Wednesday
10:00-11:45
L6 Neural nets, MichaelNielsen2019, L6-2020, LN pages 52-58, see also L6-2019  
  Wednesday
13:15-15:00
E6   Image segmentation using mixture models
Week 4/18 Monday
10:00-11:45
L7 Support vector machines, Markov random fields, Point processes L7-2020, LN pages 56-59, 60-67, 86-90, see also L7-2019  
  Monday
13:15-15:00
E7   Image filtering
Wednesday
10:00-11:45
L8 Point process analysis, Marked point processes, Warping and matching, Two-colour microrarrays L8-2020, LN pages 90-109, see also L8-2019  
  Wednesday
13:15-15:00
E8   Simulation of MRFs
Week 5/19 Monday
10:00-11:45
L9 Two-dimensional electrophoresis, Point processes with noise: forestry applications L9-2020, LN pages110-129, see also  L9-2019  
  Monday
13:15-15:00
E9   Estimation and classification using MRFs
  Wednesday
10:00-11:45
L10 Diffusion applications: FRAP, Particle concentration estimation L10-2020, LN pages 130-145, see also  L10-2019  
  Wednesday
13:15-15:00
E10   Image classification
Week 6/20 Monday
10:00-11:45
L11 RICS and SPRIA, analysis of TEM images, L11-2020, LN pages 145-167, see also L11-2019
LN pages 145-167
 
  Monday
13:15-15:00
E11   Work on projects
  Wednesday
10:00-11:45
L12 Point processes
LN 6,7
 
  Wednesday
13:15-15:00
E12   Work on projects
Week 7/21 Monday
10:00-11:45
L13 Project seminars  
  Monday
13:15-15:00
E13   Project seminars in room
  Wednesday
10:00-11:45
L14 Project seminars  
  Wednesday
13:15-15:00
E14   Project seminars in room

Week
8/22

Monday
10:00-11:45

 

Wednesday
10:00-11:45

 

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Computer labs

The exercises will be done in Matlab, and some knowledge of Matlab is assumed. If you need an introduction, see Learning MATLAB, Tobin A. Driscoll ISBN: 978-0-898716-83-2 (The book is published by SIAM).

Most computer exercises will use functions written specifically for this course. These are collected in the following file: TMS016_Matlab.zip. Download this file and add the path to the folder in matlab: addpath('path_to_folder'). Then run the command tms016path to set the path to the files.

Data used in the exercies are colleded here: TMS016_Data.zip.


Reference literature:

Learning MATLAB, Tobin A. Driscoll ISBN: 978-0-898716-83-2 (The book is published by SIAM).

 

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Course summary:

Date Details Due