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.
  • Enrolment Statistics (Spring 2022, recent)
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