Module Description

Module: Mathematical Image Processing

Courses:

TitleTypeHrs/WeekPeriod
Mathematical Image ProcessingLecture3Winter Semester
Mathematical Image ProcessingRecitation Section (small)1Winter Semester

Module Responsibility:

Prof. Marko Lindner

Admission Requirements:

None

Recommended Previous Knowledge:

  • Analysis: partial derivatives, gradient, directional derivative
  • Linear Algebra: eigenvalues, least squares solution of a linear system

Educational Objectives:

Professional Competence

Theoretical Knowledge

Students are able to 

  • characterize and compare diffusion equations
  • explain elementary methods of image processing
  • explain methods of image segmentation and registration
  • sketch and interrelate basic concepts of functional analysis 
Capabilities

Students are able to 

  • implement and apply elementary methods of image processing  
  • explain and apply modern methods of image processing

Personal Competence

Social Competence

Students are able to work together in heterogeneously composed teams (i.e., teams from different study programs and background knowledge) and to explain theoretical foundations.

Autonomy
  • Students are capable of checking their understanding of complex concepts on their own. They can specify open questions precisely and know where to get help in solving them.
  • Students have developed sufficient persistence to be able to work for longer periods in a goal-oriented manner on hard problems.

ECTS-Credit Points Module:

6 ECTS

Examination:

Oral exam

Workload in Hours:

Independent Study Time: 124, Study Time in Lecture: 56


Course: Mathematical Image Processing

Lecturer:

Marko Lindner

Language:

German & English

Period:

Winter Semester

Content:

  • basic methods of image processing
  • smoothing filters
  • the diffusion / heat equation
  • variational formulations in image processing
  • edge detection
  • de-convolution
  • inpainting
  • image segmentation
  • image registration

Literature:

Bredies/Lorenz: Mathematische Bildverarbeitung

ECTS-Credit Points Course:

6 ECTS