FI:PA166 Advanced Image Processing - Course Information

PA166 Advanced Methods of Digital Image Processing

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 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

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-IN)
• Informatics (programme FI, M-IN)
• Informatics (programme FI, N-IN)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

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/

• Enrolment Statistics (Spring 2008, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2008/PA166