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 Integral and Discrete Transforms in Image Processing 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, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2007, recent)
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