Computer Methods of Image Processing
FSI-9MZOAcad. year: 2016/2017Winter semesterNot applicable.. year of study1 credit
This course covers the subject of classical and digital photogtaphy, image processing and analysis by means of computer. The course familiarises PhD students with the digital image processing theory and selected topics of image analysis. It focuses on digital images representation and reconstruction, filtration in frequency and spatial domain, noise analysis and filtration, image enhancement, image segmentation, objects analysis and recognition, analysis of multi-spectral images.
Learning outcomes of the course unit
Basic knowledge of classic and digital photography, modern mathematical methods of image processing, image analysis and pattern recognition.
Real and complex analysis, functional analysis, basic knowledge of programming
Recommended optional programme components
Recommended or required reading
Klíma, M.; Bernas, M.; Hozman J.; Dvořák, P.: Zpracování obrazové informace. ČVUT Praha
Pratt, W. K.: Digital Image Processing. Wiley, New York
Druckmüller, M.; Heriban, P._: Digital Image Processing System 5.0. SOFO Brno
Starck, J.L. ; Murtagh, F.; Bijaoui, A.: Image Processing and Data Analysis. Cambridge Univesity Press
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline.
Assesment methods and criteria linked to learning outcomes
Language of instruction
The aim of the course is to provide students with information about modern mathematical method of image processing.
Specification of controlled education, way of implementation and compensation for absences
Missed lessons can be compensated by individual consultations.
Type of course unit
20 hours, optionally
Teacher / Lecturer
1. Principles of classic and digital photography
2. Numeric image representation, graphics formats, image data compression
3. Images reconstruction, statistical image characteristics
4. Pixel values transforms
5. Convolution, space domain filtration
6. Fourier transform, frequency domain filtration
7. Low-pass and high-pass filters, nonlinear filters
8. Adaptive filters
9. Additive noise - analysis and filtration
10. Impulse noise - analysis and filtration
11. Image segmentation
12. Object analysis
13. Pattern recognition and object classification