FI:PA173 Mathematical Morphology - Course Information
PA173 Mathematical MorphologyFaculty of Informatics
- Extent and Intensity
- 2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- 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
- Thu 8:00–9:50 B411
- Timetable of Seminar Groups:
- Knowledge at the level of course PV131 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.
- fields of study / plans the course is directly associated with
- Applied Informatics (programme FI, N-AP)
- Information Technology Security (programme FI, N-IN)
- Bioinformatics (programme FI, N-AP)
- Information Systems (programme FI, N-IN)
- Informatics (eng.) (programme FI, D-IN4)
- Informatics (programme FI, D-IN4)
- Parallel and Distributed Systems (programme FI, N-IN)
- Computer Graphics (programme FI, N-IN)
- Computer Networks and Communication (programme FI, N-IN)
- Computer Systems and Technologies (eng.) (programme FI, D-IN4)
- Computer Systems and Technologies (programme FI, D-IN4)
- Computer Systems (programme FI, N-IN)
- Embedded Systems (eng.) (programme FI, N-IN)
- Embedded Systems (programme FI, N-IN)
- Service Science, Management and Engineering (eng.) (programme FI, N-AP)
- Service Science, Management and Engineering (programme FI, N-AP)
- Social Informatics (programme FI, B-AP)
- Theoretical Informatics (programme FI, N-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
- Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
- Image Processing (programme FI, N-AP)
- Course objectives
- 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.
- 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
- 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 excercises 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 8 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
- Further Comments
- Study Materials
The course is taught annually.