Probabilistic Methods in Image Analysis
Lecturer:
Martin Schmidt
Examiner:
Prof. Dr. Joachim Weickert
Summer term 2016
Lectures (2h) with exercises and assignments (2h);
6 ECTS points
Lectures:
Wednesday, 1416 c.t.,
Building E1.3, Lecture Hall 003
Tutorials:
Friday, 1416 c.t.,
Building E1.3, Lecture Hall 001
First lecture:
Wednesday, April 20, 2016
Building E1.3, Lecture Hall 003
First tutorial:
Friday, May 6, 2016,
Building E1.3, Lecture Hall 001
News –
Prerequisites –
Synopsis –
Assessments / Exams –
Course Material –
Literature
NEWS:
The results
of the second written exam are now online.
NEWS:
Opportunity for exam inspection:
Friday, September 23, Room 4.10, Building E1 7, 10:00 a.m.  10:30 a.m.
NEWS:
The results
of the first written exam are now online.
NEWS:
Opportunity for exam inspection:
Wednesday, August 10, Room 4.10, Building E1 7, 2:00 p.m.  3:00 p.m.
Please register in the
HISPOS system
for this lecture.
This course is suitable for students of mathematics, physics or computer
science who completed their undergraduate studies in mathematics.
Knowledge of probability theory or statistics is
helpful but not required. The lectures will be given in English.
Hence passive knowledge of English is necessary.
Probabilistic techniques are employed quite successfully
in the processing and analysis of images, however, they also
play a vital role in pattern classification,
data mining and learning theory.
In this course we will discuss
 basic notions from probability theory and statistics as well as from
image processing
 histogram based image analysis and enhancement methods
 the probabilistic background of the KarhunenLoeve expansion
used for data compression, for example
 independent component analysis and applications
 the notion of entropy in image registration
 and, if time permits, we will give an introduction to the basic ideas of
Markov random fields and simulated annealing.
The first written exam took place on
Friday, July 29, 2016 from 14:00 to 16:00,
in Building E1.3, Lecture Halls 001.
The results of the first written exam can be found
here, and the corresponding
distribution of points and grades
here.
The second written exam takes place on
Wednesday, September 21, 2016 from 14:00 to 16:00,
in Building E1.3, Lecture Halls 001.
The results of the second written exam can be found
here.
The homework assignments are intended to be solved at home and have to be
submitted in the lecture break, or earlier. In order to qualify for the
exams you must obtain 50% of the possible points on average.
In case of qualification, you are allowed to take part in both exams.
The better grade counts, but each exam will count as an attempt
individually.
Both exams will be closed book exams. You will have to follow these rules:

You are allowed and obliged to bring two things
to your desk only: Your student ID card (Studierendenausweis)
and a ballpen that has no function other than writing.
Everything else has to be deposited at the walls of the exam hall.

In particular, electronic devices (including your cell phone), bags,
jackets, briefcases, lecture notes, homework and
classroom work solutions, handwritten notes, books, dictionaries, and
paper are not allowed at your desk.

Please keep your student ID card ready for an attendance check during
the exam.
 Do not use pencils or pens that are erasable with a normal rubber.
 You are not allowed to take anything with you that contains
information about the exam.
A violation of this rule means failing the
course.
 You must stay until the exam is completely over.
Participants of the course can download the course material
(access is passwordprotected).
Lecture slides
Date  Topic  Lecture

20.4.  Organisational Issues 
Notes 
20.4.  Images, and Image Degradations 
Lecture 01 
27.4.  Elements of Probability Theory I 
Lecture 02 
4.5.  Histogram Based Operations on Images I 
Lecture 03 (Update 1: typo on slide 15) 
11.5.  Elements of Probability Theory II 
Lecture 04 
18.5.  Histogram Based Operations on Images II 
Lecture 05 
25.5.  Elements of Probability Theory III 
Lecture 06 
1.6.  Estimation in Statistics: Parzen and ML 
Lecture 07 
8.6.  Registration by Maximization of Mutual Information 
Lecture 08 
15.6.  Elements of Probability Theory IV 
Lecture 09 (Update 1: several typos) 
22.6.  Markov Chains in Image Processing 
Lecture 10 
29.6.  Minimally Stochastic Approach to Singular
Diffusion Equations 
Lecture 11 
6.7.  Singular Value Decomposition and
Principal Component Analysis 
Lecture 12 
13.7.  Markov Random Fields and Texture 
Lecture 13 
20.7.  Summary 
Lecture 14 
Homework Assignments
Date 
Topic 
Submission Deadline 
27.4. 
Assignment 1 
May 4 
4.5. 
Assignment 2 
May 11 
11.5. 
Assignment 3 
May 18 
18.5. 
Assignment 4 
May 25 
25.5. 
Assignment 5 
June 1 
8.6 
Assignment 6 
June 15 
15.6 
Assignment 7 
June 22 
22.6 
Assignment 8 
June 29 
29.6 
Assignment 9 
July 6 
6.7 
Assignment 10 
July 13 
14.7 
Assignment 11 
July 21 
Relevant references will be provided in the lecture.
