Numerical Methods of Image Analysis (FSI-TNM-A)

Academic year 2018/2019
Supervisor: prof. RNDr. Miloslav Druckmüller, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: English
Aims of the course unit:
The aim of the course is to provide students with information about modern mathematical method of image processing, including programming techniques.
Learning outcomes and competences:
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
Course contents:
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.
Teaching methods and criteria:
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.
Controlled participation in lessons:
Missed lessons can be compensated for via make-up topics of exercises.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture 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 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
Literature - fundamental:
1. Pratt, W. K.: Digital Image Processing (Second Edition), New York: Wiley 1991
2. Petrou, M., and Bosdogianni, P., Image Processing: The Fundamentals, John Wiley& Sons, 1999.
Literature - recommended:
1. Klíma, M.; Bernas, M.; Hozman J.; Dvořák, P. : Zpracování obrazové informace, , 0
2. Druckmüller, M.; Heriban, P.: Digital Image Processing System 5.0, , 0
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2A-A full-time study M-MAI Mathematical Engineering -- Cr,Ex 4 Compulsory 2 2 S