Course detail
Computer Methods of Image Processing
FSI-9MZO Acad. year: 2022/2023 Winter semester
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.
Language of instruction
Czech
Supervisor
Department
Learning outcomes of the course unit
Basic knowledge of classic and digital photography, modern mathematical methods of image processing, image analysis and pattern recognition.
Prerequisites
Real and complex analysis, functional analysis, basic knowledge of programming
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
Written exam
Aims
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.
The study programmes with the given course
Programme D-IME-P: Applied Mechanics, Doctoral, recommended course
Programme D-ENE-P: Power Engineering, Doctoral, recommended course
Programme D-IME-K: Applied Mechanics, Doctoral, recommended course
Programme D-ENE-K: Power Engineering, Doctoral, recommended course
Type of course unit
Lecture
20 hours, optionally
Teacher / Lecturer
Syllabus
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