Course detail

Numerical Methods of Image Analysis

FSI-TNM-AAcad. year: 2025/2026

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

5

Mode of study

Not applicable.

Guarantor

prof. RNDr. Miloslav Druckmüller, CSc.

Department

Institute of Mathematics (ÚM)

Entry knowledge

Real and complex analysis, functional analysis, basic knowledge of programming

Rules for evaluation and completion of the course

Graded course-unit redit is awarded on condition of having passed a written test, and submitted a semester work.
Missed lessons can be compensated for via make-up topics of exercises.

Aims

The aim of the course is to provide students with information about modern mathematical method of image processing, including programming techniques.
Basic knowledge of classic and digital photography, modern mathematical methods of image processing,
image analysis and pattern recognition.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Petrou, M., and Bosdogianni, P., Image Processing: The Fundamentals, John Wiley& Sons, 1999.
Pratt, W. K.: Digital Image Processing (Second Edition), New York: Wiley 1991

Recommended reading

Druckmüller, M.; Heriban, P.: Digital Image Processing System 5.0, , 0
Klíma, M.; Bernas, M.; Hozman J.; Dvořák, P. : Zpracování obrazové informace, , 0

Classification of course in study plans

  • Programme N-AIM-A Master's 2 year of study, summer semester, compulsory-optional
  • Programme N-MAI-A Master's 1 year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. RNDr. Miloslav Druckmüller, CSc.

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 hod., compulsory

Teacher / Lecturer

prof. RNDr. Miloslav Druckmüller, CSc.

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