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Last update: Fialová Jana (15.01.2018)
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Last update: Fialová Jana (15.01.2018)
Students will be able to:
save image data correctly and use colour models properly with regards to the following image data processing
handle basic tasks in image analysis
aply basic segmentation methods
use methods of frequency analysis for noise rejection and image reconstruction
assess possibilities of image processing with a goal of desired information extraction |
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Last update: Fialová Jana (15.01.2018)
R:Barrett H., Myers K., Foundations of Image Science,Wiley,New Jersey,2004,0471153001 A:Burger W.,Burge M., Digital Image Processing,Springer,Hagengerg,2008,9781846283796 A:Gonzales R.,Woods R.,Digital Image Processing,Prentice Hall,New Jersey,2008,9780135052679 |
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Last update: Fialová Jana (15.01.2018)
http://uprt.vscht.cz/mudrova/ip |
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Last update: Fialová Jana (15.01.2018)
The subject is closed by examination and credits. It is necessary to actively take part in lessons and seminars Level of student assesment depends on oral presentation and two inidividual projects solved during term. |
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Last update: Fialová Jana (15.01.2018)
1. Basic principles of raster image representation 2. Colour models, palettes, colour reduction, dithering, halftonnig 3. Histogram and its processing, contrast, brightness, gamma correction 4. Alpha blending, binary images use 5. Most important graphic formats - survey, methods of image compression 6. Geometric image transformations, interpolation in image 7. Image registration, warping, morphing 8. Methods of image acquisition and connected problems 9. Functional image transformations and their use 10. Methods of image filtering, noise rejection 11. Edge detection and its applications 12. Methods o f image sharpening 13. Mathematical morphology 14. Discussion about individual projects |
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Last update: Fialová Jana (15.01.2018)
Computer Science |
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Last update: Mudrová Martina Ing. Ph.D. (14.02.2018)
The subject is closed by examination and credits. It is necessary to actively take part in lessons and seminars Level of student assesment depends on oral presentation and two inidividual projects solved during term. |
Teaching methods | ||||
Activity | Credits | Hours | ||
Konzultace s vyučujícími | 0.2 | 6 | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 0.5 | 14 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1.3 | 35 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 139 / 140 |