Course details

Image Processing

ZPO Acad. year 2020/2021 Summer semester 5 credits

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

News

Dear students,


this is an information about teaching during the state of emergency in Czech Republic during the corona virus outbreak. 

  • Contact lectures are cancelled but the teaching will be online according to the standard timetable, you will get instructions for lecture access and they also will be available at this course page.
  • Homeworks will be organised "as usual" (electronically).
  • Test will be taken as planned bude most probably remotely/electronically and you will get instructions.
  • The project defence will probably be done through a teleconference if the situation will not permit better option.
  • Regarding the exam - we hope that this will be organised "normally" in a contact form but this cannot, of course, be guaranteed now. Anyhow, you will get instructions.
  • If you do see 1.5. as a date of some activity, this means "has not been decided yet".
  • The overview of lectures is preliminary and it can change during the semester.

Best regards and take care of yourselves.
Pavel Zemčík

Guarantor

Deputy Guarantor

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 exam, 10 mid-term test, 39 projects

Department

Lecturer

Instructor

Subject specific learning outcomes and competences

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.

Generic learning outcomes and competences

Students will improve their teamwork skills and in exploitation of "C" language.

Learning objectives

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.

Prerequisites

Prerequisite kwnowledge and skills

The C programming language and fundamentals of computer graphics.

Study literature

  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1 
  • Šonka, M., Hlaváč, V., Boyle, R.: Image processing, Analysis, and Machine Vision, THOMSON 2013, ISBN-13: 978-9386858146
  • Jahne, B.: Handbook of Computer Vision and Applications, Academic Press, 1999, ISBN 0-12-379770-5

Fundamental literature

  • 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

Syllabus of lectures

  1. Introduction, representation of image, linear filtration  (12. 2. 2021 Zemčík slidesslidesslidesdemo)
  2. Point image transforms (19. 2. 2021 Beran slidesdemo.zip)
  3. Image acquisition (26. 2. 2021 Zemčík slides)
  4. Edge detection, segmentation (5. 3. 2020 Beran slidesexamples)
  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 slidesdemo)
  12. Lecture from industry, motion analysis (30. 4. 2021, Zoner company)
  13. Conclusion (7. 5. 2021, Zemčík/Beran slides)

Syllabus - others, projects and individual work of students

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

Progress assessment

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

Schedule

DayTypeWeeksRoomStartEndLect.grpGroupsInfo
Frilecturelectures E112 14:0015:50 1MIT 2MIT NVIZ xx

Course inclusion in study plans

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