Course detail

Numerical Methods of Image Analysis

FSI-TNMCompulsoryMaster's (2nd cycle)Acad. year: 2016/2017Summer semester1, 2. year of study4  credits

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.

Learning outcomes of the course unit

Basic knowledge of classic and digital photography, modern mathematical methods of image processing,
image analysis and pattern recognition.

Mode of delivery

90 % face-to-face, 10 % distance learning

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Klíma, M.; Bernas, M.; Hozman J.; Dvořák, P. : Zpracování obrazové informace, , 0
Pratt, W. K.: Digital Image Processing (Second Edition), New York: Wiley 1991
Druckmüller, M.; Heriban, P.: Digital Image Processing System 5.0, , 0
Petrou, M., and Bosdogianni, P., Image Processing: The Fundamentals, John Wiley& Sons, 1999.

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.

Language of instruction

Czech

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.

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

seminars in computer labs

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