PA229 Digital Image Processing

Faculty of Informatics
Autumn 2024

The course is not taught in Autumn 2024

Extent and Intensity
2/2/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
doc. RNDr. Martin Maška, Ph.D. (seminar tutor)
doc. RNDr. David Svoboda, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Michal Kozubek, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: prof. RNDr. Michal Kozubek, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable of Seminar Groups
PA229/01: No timetable has been entered into IS. M. Maška, D. Svoboda
Prerequisites
PV291 Introduction to DSP
Required knowledge: English, foundations of mathematics, linear algebra, calculus and basics of image processing at the level of PB130 + PV291 courses.
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
Course objectives
This course aims to broaden the knowledge of the basics of digital signal and image processing gained in the PB130 + PV291 courses. The students will gain an overview of the available techniques and possibilities in this field. They will learn image transforms, detection/segmentation algorithms and approaches to image/object classification. They will be able to perform the basic techniques and apply them in practice. The lecture serves as the base for all those who want to attend to the topic in more detail.
Learning outcomes
The student will be able to:
- formulate basic principles of digital image processing;
- describe mutual relations between the image analysis in spatial and frequency domains;
- realize basic workflows using a computer;
- suggest and apply suitable workflows for a given problem of image analysis;
Syllabus
  • Acquisition of 2D and 3D image data
  • Continuous convolution, PSF, OTF.
  • Fourier Transform in 2D and 3D, filtering in frequency domain
  • Relation of filters in spatial and frequency domains
  • Nonlinear image filtering
  • Steerable filters
  • Multi-scale analysis, Discrete Wavelet Transform in 2D
  • Hough and Radon Transforms
  • Image compression standards (JPEG, JPEG2000, H.265)
  • Image restoration
  • Image segmentation
  • Classification of images and objects
  • Advanced methods of digital image processing
Literature
    recommended literature
  • GONZALEZ, Rafael C. and Richard E. WOODS. Digital image processing. Fourth edition. New York, NY: Pearson, 2018, 1019 stran. ISBN 9781292223049. info
  • PRATT, William K. Digital image processing : PIKS scientific inside. 4th ed. Hoboken, N.J.: Wiley-interscience, 2007, xix, 782. ISBN 9780471767770. info
  • ŠONKA, Milan, Václav HLAVÁČ and Roger BOYLE. Image processing, analysis, and machine vision. 3rd ed. Toronto: Thomson, 2008, xxv, 829. ISBN 9780495082521. info
Teaching methods
Lectures followed by class exercises in a computer room to gain hands-on experience.
Assessment methods
Lectures and study materials in English. Mandatory practicals (labs) on computers with compulsory homework. Written final exam, no materials allowed.
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
Teacher's information
https://cbia.fi.muni.cz/education/
The course will start in Autumn 2024.
  • Permalink: https://is.muni.cz/course/fi/autumn2024/PA229