Course detail

Image Processing

FIT-ZPOAcad. year: 2024/2025

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

Czech

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)

Entry knowledge

The C programming language and fundamentals of computer graphics.

Rules for evaluation and completion of the course

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

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.
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.

Study aids

Not applicable.

Prerequisites and corequisites

Basic 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

Recommended 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

Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, OReilly 2008, ISBN: 978-0596516130

Classification of course in study plans

  • Programme MITAI Master's

    specialization NGRI , 0 year of study, summer semester, elective
    specialization NADE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NMAT , 0 year of study, summer semester, elective
    specialization NSEC , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, elective
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NVER , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NISY , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NBIO , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, compulsory

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
  2. Linear filtration
  3. Image acquisition
  4. Discrete image transforms, FFT, relationship with filtering
  5. Point image transforms
  6. Edge detection, segmentation
  7. Resampling, warping, morphing
  8. DCT, Wavelets
  9. Watermarks
  10. Image distortion, types of noise
  11. Optimal filtration
  12. Mathematical Morphology
  13. Motion analysis, conclusion

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Vítězslav Beran, Ph.D.

Syllabus

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