Course detail

Image Processing

FIT-ZPOAcad. year: 2018/2019

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

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.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • Hlaváč, V., Šonka, M.: Počítačové vidění, GRADA, 1992, ISBN 80-85424-67-3

  • 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

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Mid-term test, individual project.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

    Syllabus of lectures:
    1. Introduction to image processing
    2. Image data acquiring
    3. Point image transforms
    4. Discrete image transforms
    5. Linear image filtering
    6. Image distortion, types of noise
    7. Optimal filtering
    8. Nonlinear image filtering
    9. Watermarks
    10. Edge detection, segmentation
    11. Movement analysis
    12. Image compression, lossy, looseless
    13. Future of image processing

    Syllabus - others, projects and individual work of students:
    1. Individually assigned project for the whole duration of the course.

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

Syllabus



  1. Introduction, representation of image, linear filtration 
    (8. 2. 2019 Zemčík slides, slides, demo)


  2. Image acquisition
    (15. 2. 2019 Zemčík? slides)


  3. Discrete image transforms, FFT, relationship with filtering(Zemčík 22. 2. 2019 slajdy a slides)


  4. Point image transforms
    (1. 3. 2019 Beran slides, demo.zip)


  5. Edge detection, segmentation
    (8. 3. 2019 Beran slides, examples)


  6. Resampling, warping, morphing (15. 3. 2019 Zemčík slides)


  7. DCT, Wavelets (22. 3. 2019 Bařina slides)


  8. Watermarks (29. 3. 2019 Mlích slides, demo)


  9. Test + project status presentation (5. 4. 2019 Beran)


  10. Image distortion, types of noise, optimal filtration (12. 4. 2019 Španěl slides)


  11. no lecture - Good Friday (19. 4. 2019)
  12. Project defences + misc. (26. 4. 2019 Beran)


  13. Matematical morphology, motion analysis, conclusion (3.5. Španěl slides)


Projects

26 hours, compulsory

Teacher / Lecturer

Syllabus


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

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