Room for the Tutorial

Geschrieben am 04.11.23 von Kristina Schaefer

The tutorials will take place in seminar room 3 in building E.2.5 (also called U11) starting on the 06.11.23.

Lectures Slides with 1 Slide per Page

Geschrieben am 03.11.23 von Kristina Schaefer

After a request in yesterday's lecture, I have added a second category of slides in the materials section of the CMS. You can now download the slides with 1 or 2 slides per page, depending on your personal preference. 

Mathematical Morphology in Image Analysis

Course Description

Mathematical morphology is a powerful class of images processing methods based on shape information of image objects. Its techniques have been applied successfully to a variety of image processing tasks including image denoising, segmentation, skeletons and many more. This lectures gives an introduction to a variety of morphological techniques.

The course is designed as a supplement for image processing lectures, to be attended before, after or parallel to them. After the lecture, participants should understand the foundations of classical and PDE-based morphology and be able to apply morphological operators to image processing tasks.

General Information

  • Lecturer: Kristina Schaefer
  • Examiner: Prof. Joachim Weickert
  • advanced lecture
  • Credit Points: 6


  • on site
  • Thursday 14-16 HS003 E.1.3
  • first lecture: 26.10.2023


  • on site
  • Monday 12-14 SR 3 in E.2.5 (also called U11)
  • first tutorial: 06.11.2023


  • written exams
  • closed book
  • If you participate in both exams, the better grades counts.
  • first exam: TBD
  • second exam: TBD


Basic mathematics courses (such as Mathematik für Informatiker I-III) are recommended. Understanding English is necessary. The lecture "Image Processing and Computer Vision" is recomended. 


The course does not follow a specific book, but the following reference can be useful:

  • R. C. Gonzalez, R. E. Woods,  Digital Image Processing. Addison-Wesley, International Edition, 2017.

  • P. Soille, Morphological Image Analysis. Principles and Applications. Second edition. Springer-Verlag, Berlin Heidelberg 2004.

  • R. Kimmel, Numerical Geometry of Images. Theory, Algorithms, and Applications. First edition. Springer New York.

  • G. Sapiro, Geometric Partial Differential Equations and Image Analysis. First Edition. Cambridge University Press.

You can find these references in the Semesterapparat of the library.

Further references will be given during the lecture.




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