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Lessons and laboratory works are devoted to basic principles of acquisition, storage and processing of image digital data. There are emphasized methods which can be used in engineering image data processing in addition to general techniques.
Lessons represent and operate with terms like 2D Discrete Function, Sampling, Compression, Colour Processing, Image adjustment, Image Analysis in the Image and Frequency Area, Reconstruction, Geometric Transformations, Image Registration, Objects Detection, Segmentation and Classification.
Methods of discrete mathematics, statistics, Fourier transform, mathematical morphology and others constitute basic tools of image processing.
Practical projects are solved in the Matlab system including its Image Processing Toolbox.
Last update: Pátková Vlasta (20.04.2018)
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Students will be able to:
• aply selected advanced methods in image segmentation, registration and reconstruction • assess critically possibilities of image processing methods application • aply and interpret advanced methods of frequency image analysis Last update: Pátková Vlasta (20.04.2018)
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Zápočet z předmětu je podmíněn aktivní účastí na cvičení. Vypracování a obhajoba projektů: 0 - 60 bodů Ústní zkouška: 0-40 bodů Celkové bodové hodnocení: 100-90 A, 89-80 B, 79-70 C, 69-60 D, 59-50 E, méně než 50 F. Last update: Kohout Jan (13.02.2024)
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R:Gonzales R.,Woods R.,Digital Image Processing,Prentice Hall,New Jersey,2008,9780135052679
A: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 Last update: Pátková Vlasta (20.04.2018)
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Credits are conditioned by activity during lessons and seminars Level of student assessment depends on 1. Team project, 2. Examination. Last update: Kohout Jan (13.02.2024)
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1. FT and its application 2. Image Reconstruction 3. Principal Component Analysis 4. Object Detection 5. Advanced Methods in Colour Reduction 6. Independent Component Analysis 7. Noise Reduction 8. 2D Interpolation 9. Image Registration 10. 2D Wavelet Tarnsform 11. Texture Classification 12. Grayscale Mathematical Morphology 13. Image Segmentation 14. Conclusion
Last update: Pátková Vlasta (20.04.2018)
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https://e-learning.vscht.cz/course/view.php?id=2983 Last update: Kohout Jan (20.02.2024)
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| Teaching methods | ||||
| Activity | Credits | Hours | ||
| Účast na přednáškách | 0.5 | 14 | ||
| Práce na individuálním projektu | 1.5 | 42 | ||
| Příprava na zkoušku a její absolvování | 1 | 28 | ||
| Účast na seminářích | 1 | 28 | ||
| 4 / 4 | 112 / 112 | |||