Course detail

Image Processing (in English)

FIT-ZPOeAcad. year: 2022/2023

Introduction to image processing, image acquiring, point and discrete image transforms, linear image filtering, image distortions, types of noise, optimal image filtering, non-linear image filtering, watermarks, edge detection, segmentation, motion analysis, loseless and lossy image compression

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Dr. Ing. Pavel Zemčík, dr. h. c.

Department

Department of Computer Graphics and Multimedia (UPGM)

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

The students will get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). They will learn how to apply such knowledge on real examples of image processing tasks. They will also get acquainted with "higher" imaging algorithms. Finally, they will learn how to practically program image processing applications through projects.
Students will improve their teamwork skills and in exploitation of "C" language.

Prerequisites


Programming language C, basic knowledge of computer graphics, mathematical
analysis and linear algebra.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Mid-term exam - 10 points.
  • Individual project - 39 points.
  • Final exam - 51 points.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

To get acquainted with the image processing basics theory (transformations, filtration, noise reduction, etc.). To learn how to apply such knowledge on real examples of image processing tasks. To get acquainted with "higher" imaging algorithms. To learn kow to practically program image processing applications through projects.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Basic literature

Not applicable.

Recommended reading

Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, OReilly 2008, ISBN: 978-0596516130 (EN)
Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3 (CS)
IEEE Multimedia, IEEE, USA - series of journals- various articles (EN)
Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5 (EN)
Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1 (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-MGR-1H Master's

    branch MGH , 1 year of study, summer semester, recommended course

  • Programme IT-MSC-2 Master's

    branch MGMe , 1 year of study, summer semester, compulsory

  • Programme MIT-EN Master's 0 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. David Bařina, Ph.D.
doc. Ing. Vítězslav Beran, Ph.D.
Ing. Michal Španěl, Ph.D.
prof. Dr. Ing. Pavel Zemčík, dr. h. c.

Syllabus

  1. Introduction, representation of image, linear filtration.
  2. Image acquisition.
  3. Discrete image transforms, FFT, relationship with filtering. 
  4. Point image transforms.
  5. Edge detection, segmentation.
  6. Resampling, warping, morphing.
  7. DCT, Wavelets.
  8. Watermarks.
  9. Test + project status presentation.
  10. Image distortion, types of noise, optimal filtration.
  11. Project consultations.
  12. Project preparations.
  13. Matematical morphology, motion analysis, conclusion.

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Vítězslav Beran, Ph.D.

Syllabus

Individually assigned project for the whole duration of the course.

Elearning

eLearning: currently opened course