PA173 Mathematical Morphology

Faculty of Informatics
Spring 2021
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).
Taught online.
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
Timetable
Thu 14:00–15:50 Virtuální místnost
  • Timetable of Seminar Groups:
PA173/01: Fri 10:00–11:50 Virtuální místnost, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 7/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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
Study Materials
The course is taught annually.
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 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Spring 2022
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).
Taught online.
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: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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.

PA173 Mathematical Morphology

Faculty of Informatics
Spring 2020
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).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Mgr. Jan Ježek (assistant)
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
Timetable
Mon 17. 2. to Fri 15. 5. Tue 10:00–11:50 B411
  • Timetable of Seminar Groups:
PA173/01: Mon 17. 2. to Fri 15. 5. Mon 10:00–11:50 B311, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 3/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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
Study Materials
The course is taught annually.
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 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2018
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).
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
Timetable
Thu 12:00–13:50 B411
  • Timetable of Seminar Groups:
PA173/01: Thu 8:00–9:50 B311, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 4/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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 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
English
Further Comments
Study Materials
The course is taught annually.
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, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2017
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).
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
Timetable
Tue 10:00–11:50 B411
  • Timetable of Seminar Groups:
PA173/01: Mon 10:00–11:50 B311, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 1/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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 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
English
Further Comments
Study Materials
The course is taught annually.
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 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2016
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).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Mgr. Jan Ježek (assistant)
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
Timetable
Thu 16:00–17:50 B411
  • Timetable of Seminar Groups:
PA173/01: Tue 8:00–9:50 B311, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 0/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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.
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 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
English
Further Comments
Study Materials
The course is taught annually.
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 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2015
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).
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
Timetable
Thu 14:00–15:50 B411
  • Timetable of Seminar Groups:
PA173/01: Mon 10:00–11:50 B311, P. Matula
Prerequisites
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.
The capacity limit for the course is 24 student(s).
Current registration and enrolment status: enrolled: 0/24, only registered: 0/24, only registered with preference (fields directly associated with the programme): 0/24
fields of study / plans the course is directly associated with
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.
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 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
English
Further Comments
Study Materials
The course is taught annually.
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 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2014
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).
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
Timetable
Tue 8:00–9:50 B411, Tue 12:00–13:50 B311
Prerequisites
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
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.
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 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
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2013
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).
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
Timetable
Thu 8:00–9:50 B411
  • Timetable of Seminar Groups:
PA173/01: Mon 8:00–9:50 B311, P. Matula
Prerequisites
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
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.
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 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
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2012
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).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Mon 12:00–13:50 B411, Mon 14:00–15:50 B311
Prerequisites
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
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.
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
  • 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
Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2011
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).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Timetable
Tue 10:00–11:50 B311, Wed 8:00–9:50 C416
Prerequisites
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
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.
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
  • 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
Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2010, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2010
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Timetable
Tue 8:00–9:50 B411, Tue 10:00–11:50 B311
Prerequisites
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
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.
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
  • 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
Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 2009, Autumn 2009, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Spring 2020, Spring 2021, Spring 2022.

PA173 Mathematical Morphology

Faculty of Informatics
Autumn 2009
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Timetable
Tue 12:00–13:50 C511, Tue 16:00–17:50 B311, Tue 18:00–19:50 B311
Prerequisites
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
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.
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
  • 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
Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)
Language of instruction
English
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008, Spring 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.

PA173 Mathematical Morphology

Faculty of Informatics
Spring 2009
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Timetable
Mon 11:00–11:50 B311, Mon 12:00–13:50 C416
Prerequisites
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
Course objectives
The lecture is focused on mathematical morphology in image analysis. The methods will be discussed from theoretical, application and algorithmic point of view. Students can try the methods on simple practical examples in class exercises.
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
  • Efficient implementation of morphological operators
  • Granulometry, classification, texture analysis
Literature
  • SOILLE, Pierre. Morphological Image Analysis: Principles and Applications. 2nd edition. Berlin: Springer-Verlag, 2003. ISBN 3-540-42988-3. info
Assessment methods
Written as well as oral examination. Attendance at class excercises required. Study materials in English. Teaching in English or Czech (in the case of all enrolled students prefer Czech)
Language of instruction
English
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008, 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.

PA173 Mathematical Morphology

Faculty of Informatics
Spring 2008
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Timetable
Wed 15:00–15:50 B311, Wed 16:00–17:50 B410
Prerequisites
Knowledge at the level of the lecture PA170 is assumed.
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
Course objectives
The lecture is focused on mathematical morphology in image analysis. The methods will be discussed from theoretical, application and algorithmic point of view. Students can try the methods on simple practical examples.
Syllabus
  • Structuring element and its decomposition
  • Basic morphological operators (erosion, dilation, opening, closing, top-hat, ...)
  • Granulometry
  • Hit-or-miss transform, skeletons
  • Geodesic transformations and metrics
  • Morphological reconstructions
  • Morphological filters
  • Segmentation, watershed transform, markers
  • Efficient implementation of morphological operators
Literature
  • SOILLE, Pierre. Morphological Image Analysis: Principles and Applications. 2nd edition. Berlin: Springer-Verlag, 2003. ISBN 3-540-42988-3. info
Assessment methods (in Czech)
Písemná zkouška, nutná účast na cvičeních a domácí práce.
Language of instruction
English
Further Comments
The course is taught annually.
The course is also listed under the following terms 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.

PA173 Mathematical Morphology

Faculty of Informatics
Spring 2007

The course is not taught in Spring 2007

Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Petr Matula, Ph.D.
Prerequisites
PA170 Digital Geometry
Knowledge at the level of the lecture PA170 is assumed.
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
Course objectives
The lecture is focused on mathematical morphology in image analysis. The methods will be discussed from theoretical, application and algorithmic point of view. Students can try the methods on simple practical examples.
Syllabus
  • Structuring element and its decomposition
  • Basic morphological operators (erosion, dilation, opening, closing, top-hat, ...)
  • Granulometry
  • Hit-or-miss transform, skeletons
  • Geodesic transformations and metrics
  • Morphological reconstructions
  • Morphological filters
  • Segmentation, watershed transform, markers
  • Efficient implementation of morphological operators
Literature
  • SOILLE, Pierre. Morphological Image Analysis: Principles and Applications. 2nd edition. Berlin: Springer-Verlag, 2003. ISBN 3-540-42988-3. info
Assessment methods (in Czech)
Písemná zkouška, nutná účast na cvičeních a domácí práce.
Language of instruction
Czech
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.
  • Enrolment Statistics (recent)