Course detail
Numerical Methods of Image Analysis
FSI-TNM-A Acad. year: 2021/2022 Summer semester
The course familiarises 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
English
Number of ECTS credits
4
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. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
Graded course-unit redit is awarded on condition of having passed a written test, and submitted a semester work.
Aims
The aim of the course is to provide students with information about modern mathematical method of image processing, including programming techniques.
Specification of controlled education, way of implementation and compensation for absences
Missed lessons can be compensated for via make-up topics of exercises.
The study programmes with the given course
Programme N-MAI-A: Mathematical Engineering, Master's, compulsory
Programme M2A-A: Applied Sciences in Engineering, Master's
branch M-MAI: Mathematical Engineering, compulsory
Type of course unit
Lecture
26 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
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
1. Using of ACC 6.0 image analyzer – basic principles.
2. Programming techniques in numerical image processing and analysis
3 Data compression (lossy and lossless)
4. Statistical methods of image analysis
5. Convolution, space domain filtration
6. FFT algorithm and its using in image processing
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, moment method
13. Pattern recognition and object classification