Course detail

Numerical Methods of Image Analysis

FSI-TNM-AAcad. year: 2022/2023

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

Mode of study

Not applicable.

Guarantor

prof. RNDr. Miloslav Druckmüller, CSc.

Department

Institute of Mathematics (ÚM)

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

Co-requisites

Not applicable.

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.

Course curriculum

Not applicable.

Work placements

Not applicable.

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.

Recommended optional programme components

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

Elearning

eLearning: currently opened course

Classification of course in study plans

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

  • Programme LLE Lifelong learning

    branch CZV , 1 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Mgr. Jana Procházková, Ph.D.

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

Mgr. Jana Procházková, Ph.D.

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

Elearning

eLearning: currently opened course