PA173 Mathematical Morphology

Faculty of Informatics
Spring 2025
Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
In-person direct teaching
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Prerequisites
Knowledge at the level of course PB130 Introduction to Digital Image Processing is useful.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 0/24, only registered: 5/24, only registered with preference (fields directly associated with the programme): 4/24
fields of study / plans the course is directly associated with
there are 30 fields of study the course is directly associated with, display
Course objectives
The objective of the course is to introduce mathematical morphology theory, algorithms, and applications to students interested in digital image processing.
Learning outcomes
At the end of the course students should be able to: understand and explain the principles of mathematical morphology methods and efficient algorithms for their computation; respect their properties and theoretical limits; demonstrate their usage on typical image analysis problems in various application fields; solve image analysis problems using mathematical morphology.
Syllabus
  • Structuring element and its decomposition
  • Fundamental morphological operators (erosion, dilation, opening, closing, top-hat)
  • Hit-or-miss transform, skeletons, thinning, thickening
  • Geodesic transformations and metrics
  • Morphological reconstructions
  • Morphological filters
  • Segmentation, watershed transform, markers, hierarchical segmentation
  • Efficient implementation of morphological operators
  • Granulometry, classification, texture analysis
Literature
    recommended literature
  • SOILLE, Pierre. Morphological Image Analysis: Principles and Applications. 2nd edition. Berlin: Springer-Verlag, 2003. ISBN 3-540-42988-3. info
Teaching methods
Lectures followed by class exercises in a computer room to gain hands-on experience.
Assessment methods
Attendance at class exercises required, written as well as oral examination. In the written part, 55 points can be obtained by presenting correct results of morphological transforms on given 1D functions, and 45 points in typically eight open-style questions. To continue to the oral part, the students must achieve at least 50 points from the written part. The oral part is a discussion about the written part testing understanding the main concepts.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
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