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)
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

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (eng.) (programme FI, N-IN)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-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)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Social Informatics (programme FI, B-AP)
• Theoretical Informatics (programme FI, N-IN)
• 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

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.

• Enrolment Statistics (Spring 2017, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2017/PA166