PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2021
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Taught online.
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Pavel Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Thu 8:00–9:50 Virtuální místnost
  • Timetable of Seminar Groups:
PA166/01: Thu 10:00–11:50 B311, M. Maška
Prerequisites
PB130 Intro Digital Image Processing
Knowledge at the level of the lecture PB130 Introduction to Digital Image Processing 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
there are 49 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the basics of state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Learning outcomes
At the end of the course students should be able to: understand the basics of state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion and its relation to Gaussian blurring
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algorithm, basics of level set methods
  • Level-set methods (basic numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Minimization based on graph-cuts
Literature
    recommended literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
Assessment methods
Written as well as oral examination. Attendance at class exercises 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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2022
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Taught online.
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Pavel Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Prerequisites
PB130 Intro Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 49 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the basics of state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Learning outcomes
At the end of the course students should be able to: understand the basics of state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion and its relation to Gaussian blurring
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algorithm, basics of level set methods
  • Level-set methods (basic numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Minimization based on graph-cuts
Literature
    recommended literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
Assessment methods
Written as well as oral examination. Attendance at class exercises 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 taught: every week.
The course is also listed under the following terms Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2020
Extent and Intensity
2/2. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Pavel Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Mon 17. 2. to Fri 15. 5. Wed 10:00–11:50 A319
  • Timetable of Seminar Groups:
PA166/01: Mon 17. 2. to Fri 15. 5. Wed 12:00–13:50 B311, M. Maška
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 49 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Learning outcomes
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
Assessment methods
Written as well as oral examination. Attendance at class exercises 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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2019
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Thu 21. 2. to Thu 16. 5. Thu 10:00–11:50 A218
  • Timetable of Seminar Groups:
PA166/01: Thu 21. 2. to Thu 16. 5. Thu 12:00–13:50 B311, M. Maška
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 20 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Learning outcomes
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2017
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Wed 8:00–9:50 B410
  • Timetable of Seminar Groups:
PA166/01: Wed 10:00–11:50 B311, M. Maška
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 20 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2016
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Tue 12:00–13:50 A319
  • Timetable of Seminar Groups:
PA166/01: Tue 14:00–15:50 B311, M. Maška
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 20 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2015
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Mon 8:00–9:50 A319
  • Timetable of Seminar Groups:
PA166/01: Mon 10:00–11:50 B311, M. Maška, P. Matula
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 19 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2014
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Dmitry Sorokin, Ph.D. (assistant)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Tue 12:00–13:50 B410, Tue 14:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 19 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2013
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Dmitry Sorokin, Ph.D. (assistant)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Thu 12:00–13:50 C525, Thu 14:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 19 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2012
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
Wed 8:00–9:50 B411, Wed 10:00–11:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 19 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems.
Syllabus
  • Mathematically well-founded image analysis and image processing methods (formulated in terms of Partial Differential Equations - PDE - and variational calculus)
  • Image filtering and image restoration in terms of PDE
  • Diffusion filtering
  • Variational formulation of image segmentation (Mumford-Shah functional)
  • Morphological dilation and erosion as a solution of PDE, shock filtering
  • Active contours and surfaces
  • Level-set methods
  • Optical flow
  • Image registration
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2011
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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. Pavel Matula, Ph.D.
Timetable
Wed 8:00–9:50 C416, Wed 12:00–13:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 18 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems.
Syllabus
  • Mathematically well-founded image analysis and image processing methods (formulated in terms of Partial Differential Equations - PDE - and variational calculus)
  • Image filtering and image restoration in terms of PDE
  • Diffusion filtering
  • Variational formulation of image segmentation (Mumford-Shah functional)
  • Morphological dilation and erosion as a solution of PDE, shock filtering
  • Active contours and surfaces
  • Level-set methods
  • Optical flow
  • Image registration
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2010
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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. Pavel Matula, Ph.D.
Timetable
Tue 8:00–9:50 B003, Tue 12:00–13:50 B311, Tue 14:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 21 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems.
Syllabus
  • Mathematically well-founded image analysis and image processing methods (formulated in terms of Partial Differential Equations - PDE - and variational calculus)
  • Image filtering and image restoration in terms of PDE
  • Diffusion filtering
  • Variational formulation of image segmentation (Mumford-Shah functional)
  • Morphological dilation and erosion as a solution of PDE, shock filtering
  • Active contours and surfaces
  • Level-set methods
  • Optical flow
  • Image registration
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2009
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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. Pavel Matula, Ph.D.
Timetable
Tue 12:00–13:50 B003, Tue 16:00–17:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 18 fields of study the course is directly associated with, display
Course objectives
The course is focused on state-of-the-art mathematically well-founded methods of digital image analysis and processing. No prior knowledge of numerical mathematics and functional analysis is required. Necessary mathematical fundamentals will be explained during the course. Students can try the methods at class exercises.
Syllabus
  • Mathematically well-founded image analysis and image processing methods (formulated in terms of Partial Differential Equations - PDE - and variational calculus)
  • Image filtering and image restoration in terms of PDE
  • Diffusion filtering
  • Variational formulation of image segmentation (Mumford-Shah functional)
  • Morphological dilation and erosion as a solution of PDE, shock filtering
  • Active contours and surfaces
  • Level-set methods
  • Optical flow
  • Image registration
Literature
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Assessment methods
Written as well as oral exam. Attendance at class exercises mandatory. 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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2008
Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), 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. Pavel Matula, Ph.D.
Timetable
Mon 12:00–13:50 B007, Mon 16:00–17:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing is assumed. Basic knowledge of methods from PA171 Digital Image Filtering and PV027 Optimization is advantageous.
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
there are 18 fields of study the course is directly associated with, display
Course objectives
The course is focused on state-of-the-art mathematically well-founded methods of digital image analysis and processing. No prior knowledge of numerical mathematics and functional analysis is required. Necessary mathematical fundamentals will be explained during the course. Students can try the methods on tutorials.
Syllabus
  • Mathematically well-founded image analysis and image processing methods (PDE (Partial Differential Equation) and variational methods)
  • Image filtering and image restoration using PDE
  • Diffusion filtering
  • Image segmentation as a minimization problem
  • Parametric and implicit deformable models
  • Level-set methods
  • Optical flow
  • PCA (Principle Component Analysis) methods
  • Image registration
  • Point-set registration, ICP algorithm
Literature
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
  • SINGH, Ajit, Dmitry GOLDGOF and Demetri TERZOPOULOS. Deformable models in medical image analysis. Los Alamitos: IEEE Computer Society, 1998. x, 388 s. ISBN 0-8186-8521-2. info
  • GOSHTASBY, Ardeshir. 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. Wiley-Interscience, 2005. info
Assessment methods (in Czech)
Písemná zkouška, nutná účast na cvičeních a domácí práce.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://cbia.fi.muni.cz/
The course is also listed under the following terms Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2007
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
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. Pavel Matula, Ph.D.
Timetable
Tue 12:00–13:50 B003, Tue 15:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing is assumed. Basic knowledge of methods from PA171 Digital Image Filtering and PV027 Optimization is advantageous.
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
there are 7 fields of study the course is directly associated with, display
Course objectives
The course is focused on state-of-the-art mathematically well-founded methods of digital image analysis and processing. No prior knowledge of numerical mathematics and functional analysis is required. Necessary mathematical fundamentals will be explained during the course. Students can try the methods on tutorials.
Syllabus
  • Image pyramids
  • Mathematically well-founded image analysis and image processing methods (PDE (Partial Differential Equation) and variational methods)
  • Image filtering and image restoration using PDE
  • Image segmentation as a minimization problem
  • Parametric and implicit deformable models
  • Optical flow
  • PCA (Principle Component Analysis) methods
  • Image registration
  • Point-set registration, ICP algorithm
Literature
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
  • SINGH, Ajit, Dmitry GOLDGOF and Demetri TERZOPOULOS. Deformable models in medical image analysis. Los Alamitos: IEEE Computer Society, 1998. x, 388 s. ISBN 0-8186-8521-2. info
  • GOSHTASBY, Ardeshir. 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. Wiley-Interscience, 2005. 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.
Teacher's information
http://lom.fi.muni.cz/
The course is also listed under the following terms Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2006
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing - Faculty of Informatics
Contact Person: prof. RNDr. Michal Kozubek, Ph.D.
Timetable
Tue 10:00–11:50 B204, Wed 14:00–14:50 B311, Wed 15:00–15:50 B311
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 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
there are 7 fields of study the course is directly associated with, display
Course objectives
This course is a continuation of the course PV131. The course concerns volumetric image processing, particularly advanced mathematical morphology methods and deformable models for 3D boundary extraction. Students can try the methods on practicals.
Syllabus
  • Specifics of 3D image processing
  • Basic morphological operators (erosion, dilation, opening, closing, ...)
  • Hit-or-miss transformation, skeletons
  • Geodesic transformations and metrics
  • Morphological filtering
  • Watershed transformation, markers
  • Image registration
  • Point-set registration, ICP algorithm
  • Object reconstruction
  • Parametric, implicit and discrete deformable models
Literature
  • KLETTE, Reinhard and Azriel ROSENFELD. Digital geometry: geometric methods for digital picture analysis. Amsterdam: Elsevier, 2004. 656 pp. info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
  • SOILLE, Pierre. Morphological image analysis : principles and applications. Berlin: Springer, 1999. xii, 316. ISBN 3540656715. info
  • SINGH, Ajit, Dmitry GOLDGOF and Demetri TERZOPOULOS. Deformable models in medical image analysis. Los Alamitos: IEEE Computer Society, 1998. x, 388 s. ISBN 0-8186-8521-2. info
Assessment methods (in Czech)
Písemná zkouška, nutná účast na cvičeních a domácí práce.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/lom/
The course is also listed under the following terms Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2005
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Petr Matula, Ph.D. (lecturer)
Guaranteed by
prof. PhDr. Karel Pala, CSc.
High-Resolution Cytometry Laboratory - Department of Machine Learning and Data Processing - Faculty of Informatics
Contact Person: prof. RNDr. Michal Kozubek, Ph.D.
Timetable
Thu 10:00–11:50 B204
  • Timetable of Seminar Groups:
PA166/01: Fri 10:00–10:50 B311, P. Matula
PA166/02: Fri 11:00–11:50 B311, P. Matula
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 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
there are 7 fields of study the course is directly associated with, display
Course objectives
This course is a continuation of the course PV131. The course concerns volumetric image processing, particularly advanced mathematical morphology methods and deformable models for 3D boundary extraction. Students can try the methods on practicals.
Syllabus
  • Specifics of 3D image processing
  • Basic morphological operators (erosion, dilation, opening, closing, ...)
  • Hit-or-miss transformation, skeletons
  • Geodesic transformations and metrics
  • Morphological filtering
  • Watershed transformation, markers
  • Image registration
  • Point-set registration, ICP algorithm
  • Object reconstruction
  • Parametric, implicit and discrete deformable models
Literature
  • SOILLE, Pierre. Morphological image analysis : principles and applications. Berlin: Springer, 1999. xii, 316. ISBN 3540656715. info
  • SINGH, Ajit, Dmitry GOLDGOF and Demetri TERZOPOULOS. Deformable models in medical image analysis. Los Alamitos: IEEE Computer Society, 1998. x, 388 s. ISBN 0-8186-8521-2. info
  • LOHMANN, Gabriele. Volumetric image analysis. Chichester: Wiley-Teubner, 1998. x, 243 s. ISBN 3-519-06447-2. info
Assessment methods (in Czech)
Písemná zkouška, nutná účast na cvičeních a domácí práce.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/lom/
The course is also listed under the following terms Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.

PA166 Advanced Methods of Digital Image Processing

Faculty of Informatics
Spring 2018

The course is not taught in Spring 2018

Extent and Intensity
2/2. 4 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
doc. RNDr. Pavel Matula, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Petr Matula, Ph.D.
Department of Visual Computing - Faculty of Informatics
Contact Person: doc. RNDr. Pavel Matula, Ph.D.
Supplier department: Department of Visual Computing - Faculty of Informatics
Timetable
except Thu 17. 5.
  • Timetable of Seminar Groups:
PA166/01: Thu 14:00–15:50 B311, M. Maška
Prerequisites
PV131 Digital Image Processing
Knowledge at the level of the lecture PV131 Digital Image Processing 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
there are 20 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Learning outcomes
At the end of the course students should be able to: understand the state-of-the-art mathematically well-founded methods of digital image processing; numerically solve basic partial differential equations and variational problems of digital image processing.
Syllabus
  • Image as a function, computation of differential operators
  • Linear diffusion vs. Gaussian blur
  • Nonlinear isotropic diffusion
  • Nonlinear anisotropic diffusion
  • Variational filtering
  • Mathematical morphology as a solution of PDE (dilation and erosion), shock filtering
  • Parametric active contours (snakes)
  • Fast marching algoritmus, basics of level set methods
  • Level-set methods (numerical schemes)
  • Segmentation (geodesic active contours, Mumford-Shah and Chan-Vese funkcionals)
  • Optical flow
  • Graph-cut based minimization
Literature
  • WEICKERT, Joachim. Anisotropic Diffusion in Image Processing. Stuttgart, Germany: Teubner-Verlag, 1998. URL info
  • OSHER, Stanley and Ronald FEDKIW. Level Set Methods and Dynamic Implicit Surfaces. New York: Springer-Verlag, 2003. ISBN 0-387-95482-1. info
Teaching methods
Lectures followed by class exercises in a computer room. Implementation of the key parts in C++.
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 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.
  • Enrolment Statistics (recent)