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, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2010, recent)
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