Course details

Image Processing (in English)

ZPOe Acad. year 2018/2019 Summer semester 5 credits

Current academic year

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

Guarantor

Deputy Guarantor

Language of instruction

English

Completion

Examination (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

51 exam, 10 mid-term test, 39 projects

Department

Lecturer

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


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

Study literature

  • Russ, J.C.: The Image Processing Handbook, CRC Press 1995, ISBM 0-8493-2516-1

Fundamental literature

  • Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA 1992, ISBN 80-85424-67-3
  • 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  (7. 2. 2019 Zemčík slides, slides, demo)
  2. Cancelled, will be replaced some other time: Image acquisition (14. 2. 2019 Zemčík? slides)
  3. Discrete image transforms, FFT, relationship with filtering (Zemčík 21. 2. 2019 slajdy a slides)
  4. Point image transforms (28. 2. 2019 Beran slides, demo.zip)
  5. Edge detection, segmentation (7. 3. 2019 Beran slides, examples)
  6. Resampling, warping, morphing (14. 3. 2019 Zemčík slides)
  7. DCT, Wavelets (21. 3. 2019 Bařina slides)
  8. Watermarks (28. 3. 2019 Mlích slides, demo)
  9. Test + project status presentation (4. 4. 2019 Beran)
  10. Image distortion, types of noise, optimal filtration (11. 4. 2019 Španěl slides)
  11. no lecture - Easter (working day, project consultations possible depending on interest 18. 4. 2019)
  12. Project preparations (25. 4. 2019 Beran)
  13. Matematical morphology, motion analysis, conclusion (2.5. Španěl slides)

Syllabus - others, projects and individual work of students

Individually assigned project for the whole duration of the course.

Progress assessment

Mid-term test, individual project.

Course inclusion in study plans

  • Programme IT-MSC-2, field MGMe, 1st year of study, Compulsory
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