Course detail

Image Processing

FIT-ZPOAcad. year: 2020/2021

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

Supervisor

prof. Dr. Ing. Pavel Zemčík

Department

Department of Computer Graphics and Multimedia (UPGM)

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

The C programming language and fundamentals of computer graphics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
IEEE Multimedia, IEEE, USA - série časopisů - různé články
Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5
Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Mid-term test, project (homeworks and individual project).

Language of instruction

Czech, English

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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 5 credits, elective
    branch MPV , any year of study, summer semester, 5 credits, compulsory-optional
    branch MSK , any year of study, summer semester, 5 credits, elective
    branch MIS , any year of study, summer semester, 5 credits, elective
    branch MBS , any year of study, summer semester, 5 credits, elective
    branch MIN , any year of study, summer semester, 5 credits, elective
    branch MMM , any year of study, summer semester, 5 credits, elective

  • Programme MITAI Master's

    specialization NADE , any year of study, summer semester, 5 credits, elective
    specialization NBIO , any year of study, summer semester, 5 credits, elective
    specialization NGRI , any year of study, summer semester, 5 credits, elective
    specialization NNET , any year of study, summer semester, 5 credits, elective
    specialization NVIZ , any year of study, summer semester, 5 credits, compulsory
    specialization NCPS , any year of study, summer semester, 5 credits, elective
    specialization NSEC , any year of study, summer semester, 5 credits, elective
    specialization NEMB , any year of study, summer semester, 5 credits, elective
    specialization NHPC , any year of study, summer semester, 5 credits, elective
    specialization NISD , any year of study, summer semester, 5 credits, elective
    specialization NIDE , any year of study, summer semester, 5 credits, elective
    specialization NISY , any year of study, summer semester, 5 credits, elective
    specialization NMAL , any year of study, summer semester, 5 credits, elective
    specialization NMAT , any year of study, summer semester, 5 credits, elective
    specialization NSEN , any year of study, summer semester, 5 credits, elective
    specialization NVER , any year of study, summer semester, 5 credits, elective
    specialization NSPE , any year of study, summer semester, 5 credits, elective

  • Programme IT-MGR-2 Master's

    branch MGM , 1. year of study, summer semester, 5 credits, compulsory
    branch MMI , 1. year of study, summer semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Ing. David Bařina, Ph.D.
Ing. Vítězslav Beran, Ph.D.
Ing. Michal Španěl, Ph.D.
prof. Dr. Ing. Pavel Zemčík

Syllabus

  1. Introduction, representation of image, linear filtration  (12. 2. 2021 Zemčík slides, slides, slides, demo)
  2. Point image transforms (19. 2. 2021 Beran slides, demo.zip)
  3. Image acquisition (26. 2. 2021 Zemčík slides)
  4. Edge detection, segmentation (5. 3. 2020 Beran slides, examples)
  5. Image distortion, types of noise, optimal filtration (12. 3. 2021 Španěl slides)
  6. Discrete image transforms, FFT, relationship with filtering (Zemčík 19. 3. 2021 slajdy a slides)
  7. DCT, Wavelets (26. 3. 2020 Bařina slides)
  8. Good Friday - lecture cancelled (2. 4. 2021)
  9. Resampling, warping, morphing (9. 4. 2021 Zemčík slides)
  10. Test, Project status presentation, mathematical morphology (16. 4. 2021 Beran slides)
  11. Watermarks (23. 4. 2021 Zemčík slides, demo)
  12. Lecture from industry, motion analysis (30. 4. 2021, Zoner company)
  13. Conclusion (7. 5. 2021, Zemčík/Beran slides)

Project

26 hours, compulsory

Teacher / Lecturer

Ing. Vítězslav Beran, Ph.D.

Syllabus

  1. Individually assigned project for the whole duration of the course.