Latest news
30/5: Preliminary solutions to the exam can be found here.
2/5: The second part of the project is now available below.
13/4: The first part of the project is now available below. You can start working on this after computer exercise 4 on Monday.
9/4: The lecture and exercise plan has been updated. Since we did not have time to cover the likelihoodbased approach today, this will be done on Wednesday instead, and you can then continue working on Computer Exercise 3 also on Wednesday.
9/4: You can now sign up for project groups in PINGPONG.
Welcome to the course! The schedule for the course can be found in TimeEdit.
Teachers
Course coordinator: David Bolin (david.bolin@chalmers.se)
Lab supervisor: Marco Longfils (longfils@chalmers.se)
Course literature
Most of the material covered in the course is briefly described in the Lecture notes (abbreviated LN in the schedule below) by Mats Rudemo.
During the first part of the course, we will mainly use chapters from the Handbook of Spatial Statistics, which is available as an eBook through the Chalmers library. For the second part of the course, we will also use material from
The Elements of Statistical Learning, which also is available as an eBook through the Chalmers library.
Program
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 nodes and HS denotes the Handbook of spatial statistics and EL The Elements of Statistical Learning.The lectures will always be in room MVF26, and the computer exercises in room MVF25.
Time  Lecture  Computer exercise  

Week 1/12  Monday 10:0011:45 
L1 
Introduction
LN pages 116. 

Monday 13:1515:00 
E1 
Basic image processing chalmersplatsen.jpg 

Wednesday 10:0011:45 
L2 
Random fields
HS 2.12.7 

Wednesday 13:1515:00 
E2 
Gaussian fields Matlab files (zip) 

Week 2/15  Monday 10:0011:45 
L3 
Estimation and kriging
HS 2.8, 3 

Monday 13:1515:00 
E3 
Estimation and kriging Matlab files (zip) 

Wednesday 10:0011:45 
L4 
Likelihoodbased parameter estimation
HS 4.14.3 

Wednesday 13:1515:00 
E4  Continue working on exercise 3  
Week 3/16  Monday 10:0011:45 
L5 
Gaussian Markov random fields
HS 12.1.112.1.4, parts of 12.1.7, and 13.113.2 

Monday 13:1515:00 
E5 
Image reconstruction using GMRFs Matlab files 

Wednesday 10:0011:45 
L6 
Image segmentation and mixture models
LN 1.3, 2.12.3,2.8 

Wednesday 13:1515:00 
E6 
Image segmentation using mixture models Matlab files (zip) 

Week 4/17  Monday 10:0011:45 
L7 
Feature selection and filtering
LN 1.2,1.41.6 

Monday 13:1515:00 
E7  Image filtering  
Wednesday 10:0011:45 
L8 
Discrete Markov random fields
HS 12.1.5, 12.1.8, 12.1.9 

Wednesday 13:1515:00 
E8 
Simulation of MRFs Matlab files 

Week 5/18  Wednesday 10:0011:45 
L9 
Segmentation using Markov random fields
LN 4 

Monday 13:1515:00 
E9 
Classification using Markov random fields Matlab files (zip) 

Week 6/19  Monday 10:0011:45 
L10 
Classification and machine learning LN 2.42.7, 3.2, EL 2, 12 

Monday 13:1515:00 
E10 
Image classification Matlab files (zip) 

Wednesday 10:0011:45 
L11 
Neural nets
LN 3.1, EL 11 

Wednesday 13:1515:00 
E8  Work on projects  
Week 7/20  Monday 10:0011:45 
L12 
Point processes
LN 6,7 

Monday 13:1515:00 
E12  Work on projects  
Wednesday 10:0011:45 
L13 
Repetition and exam questions Mock exam 

Wednesday 13:1515:00 
E13  Work on projects  
Week 8/21  Monday 10:0011:45 
L14  Project seminars  
Monday 13:1515:00 
E14  Work on projects  
Wednesday 10:0011:45 
L15  Project seminars  
Wednesday 13:1515:00 
E8  Work on projects 
Computer exercises
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: 9780898716832 (The book is published by SIAM).
Most computer exercises will use functions written specifically for this course. These will be linked in the schedule before each exercise.
Course requirements and representatives
The learning goals of the course can be found in the course plan.
The student representatives for the course are Erik Larsson (erlarsso@student.chalmers.se), Vincent Szolnoky (szolnoky@student.chalmers.se), and Elijah Ferreira (elijahf@student.chalmers.se).
Examination
The examination consists of two parts: One written exam at the end of the course and one project assignment. These two parts are weighted equally for the final grade. The written exam is individual whereas the project work can be done in groups of 13 students.
The project consists of three parts:
 Image reconstruction (based on computer exercises 14). Images: rosetta.jpg, titan.jpg
 Image segmentation (based on computer exercises 58). Data: permeability.mat
 Problem of your own choice.
Regarding the project report:
 Parts 1 and 2 should be documented as lab reports, describing what you did and the results you obtained but it does not need a detailed introduction and discussion.
 The ideal form of the report for Part 3 is in principle a journal paper, containing:
 Project title, author names, course name, date of report.
 Abstract/Summary: about 1015 lines.
 Introduction: Statement of problem, earlier work with references.
 Data description and source.
 Methods: Mathematical, statistical, computational, image analysis.
 Results with tables and figures.
 Discussion: include if possible here also comparison with results from literature.
 Conclusions, suggested continued studies.
 References.
Deadlines for the project:
 For Parts 1 and 2: A PDF containing these two parts should be submitted via PINGPONG at the latest May 18, 23:55. Include the Matlab code as a zipfile.
 For Part 3: A PDF containing a preliminary version of the report should be submitted together with Parts 1 and 2 at the latest May 18, 23:55. It does not need to be complete. The final version of the report should be submitted at the latest June 1, 23:55, together with the Matlab code as a zipfile, as a revision of the submitted project in PINGPONG.
Examination procedures
In Chalmers Student Portal you can read about when exams are given and what rules apply on exams at Chalmers. In addition to that, there is a schedule when exams are given for courses at University of Gothenburg.
Before the exam, it is important that you sign up for the
examination. If you study at Chalmers, you will do this by the
Chalmers Student Portal, and if you study at University of
Gothenburg, you sign up via GU's
Student Portal.
At the exam, you should be able to show valid identification.
After the exam has been graded, you can see your results in Ladok by logging on to your Student portal.
At the annual (regular) examination:
When it is practical, a separate review is arranged. The date of
the review will be announced here on the course homepage. Anyone
who can not participate in the review may thereafter retrieve
and review their exam at the Mathematical
Sciences Student office. Check that you have the right
grades and score. Any complaints about the marking must be
submitted in writing at the office, where there is a form to
fill out.
At reexamination:
Exams are reviewed and retrieved at the Mathematical
Sciences Student office. Check that you have the right
grades and score. Any complaints about the marking must be
submitted in writing at the office, where there is a form to
fill out.