Differential Equations in Image Processing and Computer Vision
Lecturer:
Prof. Dr. Joachim Weickert
Office hours: Friday, 14:15  15:15.
Coordinator of tutorials:
Dr. Bernhard Burgeth
Office hours: Tuesday, 15:00  16:00.
Summer Term 2007
Results of the second written exam are available now, see below.
Lectures (4h) with theoretical and programming exercises (2h)
(9 credit points)
Lectures: Tuesday, Friday 1113 c.t., Building E13, Lecture Hall 1
Tutorials: 2 hours each week; see below.
Prerequisites –
Synopsis –
Planned Contents –
Assignments –
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 Wednesday:
 Group T1 (Sebastiano Barbieri):
Tue, 1618, Bldg. E1.3, room SR 15 (theory) and CIP
pool room 105 (programming)
 Group W1 (Luis Pizarro):
Wed, 1416, Bldg. E2.5, lecture hall 3 (theory) and
CIP pool room 105, Bldg. E1.3 (programming)
 Group W2 (Luis Pizarro):
Wed, 1618, Bldg. E2.5, lecture hall 3 (theory) and
CIP pool room 105, Bldg. E1.3 (programming)
You could enroll
for a tutorial from Tue, Apr. 17, 2007, 14:00h
to Fri, Apr. 20, 2007, 16:00h.
The first written exam has taken place on July 24 from 2 to 5 PM
in Building E13, Lecture Halls 002 and 003.
The second
written exam has taken place on October 11 from 2 to 5 PM
in Building E13, Lecture Hall 002.
The
of the written exam can be found
here.
The following thresholds were applied in determining the grades
in the second exam:
 grade 1.0 : 37  39 points (2)
 grade 1.3 : 35  36 points (2)
 grade 1.7 : 33  34 points (0)
 grade 2.0 : 31  32 points (2)
 grade 2.3 : 29  30 points (0)
 grade 2.7 : 27  28 points (1)
 grade 3.0 : 25  26 points (0)
 grade 3.3 : 23  24 points (2)
 grade 3.7 : 21  22 points (1)
 grade 4.0 : 19  20 points (4)
 grade 5.0 : 00  18 points (2)
 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.
