Differential Equations in Image Processing and Computer Vision
Lecturer:
Prof. Dr. Joachim Weickert
Office hours: Friday, 14:15  15:15.
Coordinator of tutorials:
Dr. Stephan Didas
Office hours: Wednesday, 14:00  15:00.
Summer Term 2008
Lectures (4h) with theoretical and programming exercises (2h)
(9 credit points)
Lectures: Tuesday, Friday 1012 c.t., Building E13, Lecture Hall 1
First lecture: Tuesday, April 15, 2008
Tutorials: 2 hours each week; see below.
News:
The results of the second written exam are
online, together with the
statistics.
The inspection takes place on upcoming Wednesday, October 29, from 23 p.m. in room 3.06, building E1 1
Still, the results of the first written exam can be found
here. The statistics of the results can be found
here.
Prerequisites –
Synopsis –
Planned Contents –
Assignments –
Tutorials –
Written Exams –
References
Equally suited for students of mathematics and computer science.
Requires undergraduate knowledge in mathematics (e.g. ''Mathematik
für Informatiker IIII'') . Knowledge in image processing or differential
equations is useful, but not required. The lectures will be given
in English.
Many modern techniques in image processing and computer vision make
use of methods based on partial differential equations (PDEs) and
variational calculus. Moreover, many classical methods may be
reinterpreted as approximations of PDEbased techniques. In this
course we will get an indepth insight into these methods.
For each of these techniques, we will discuss the basic ideas
as well as theoretical and algorithmic aspects. Examples from
the fields of medical imaging and computer aided quality control
illustrate the various application possibilities.
Since this class guides its participants to many
research topics in our group, its attendance is required for
everyone who wishes to pursue a master thesis in our
group.
A combination of theoretical, programming and classroom assignments is
offered. Previous experiences have shown that they are very helpful
for understanding the methods.
Here you can download the material for the programming assignments:
Three groups are scheduled for Tuesday and Thursday:
 Group 1 (Luis Pizarro):
Tue, 1618, Bldg. E2.4, seminar room 3 (theory) and
bldg. E1 3 CIP pool room 104 (programming)
 Group 2 (Markus Mainberger):
Thu, 1214, Bldg. E1.3, seminar room 016 (theory) and
bldg. E1 3 CIP pool room 104 (programming)
 Group 3 (Markus Mainberger):
Thu, 1618, Bldg. E2.4, seminar room 3 (theory) and
bldg. E1 3 CIP pool room 104 (programming)
The tutors can be reached via the mail addresses:
dicg#  at  mia.unisaarland.de
where # has to be replaced by the group number.
You could register for the lecture and enroll for a tutorial
from Tue, Apr. 15, 2008, 14:00h
to Fri, Apr. 18, 2008, 16:00h.
The first written exam has taken place on July 22 from 2 to 5 PM
in building E2 5, lecture hall I.
The second written exam will take place on October 14 from 2 to
5 PM in building E2 5, lecture hall I.
You can bring your lecture notes and
notes from the tutorials.
You have to pass one exam, and the better grade counts.
In order to qualify for the exam you must
 attend 80% of the programming and the theoretical tutorials (we do check)
 solve 50% of all assignments (theory and programming) correctly.
Working in groups of up to 3 people is permitted, but all persons must
be in the same tutorial group.
 J. Weickert:
Anisotropic Diffusion in Image Processing.
Teubner, Stuttgart, 1998.
 G. Aubert and P. Kornprobst:
Mathematical Problems in Image Processing: Partial Differential
Equations and the Calculus of Variations.
Springer, New York, 2002.
 F. Cao:
Geometric Curve Evolutions and Image Processing.
Lecture Notes in Mathematics, Vol. 1805, Springer, Berlin, 2003.
 R. Kimmel:
The Numerical Geometry of Images.
Springer, New York, 2004.
 G. Sapiro:
Geometric Partial Differential Equations in Image Analysis.
Cambridge University Press, 2001.
 Articles from journals and conferences.
