Course detail
Numerical Methods of Image Analysis
FSI-TNMAcad. year: 2024/2025
The course familiarises students with the digital image processing theory and selected topics of image analysis. It focuses on digital image 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
Number of ECTS credits
Mode of study
Department
Entry knowledge
Rules for evaluation and completion of the course
Course-unit credit based on a written test.
Exam has a written and an oral part.
Missed lessons can be compensated via aditional exercises.
Aims
The aim of the course is to provide students with information about modern mathematical methods 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
Prerequisites and corequisites
Basic literature
Petrou, M., and Bosdogianni, P., Image Processing: The Fundamentals, John Wiley& Sons, 1999. (EN)
FOLLAND, Gerald B, 1992. Fourier analysis and its applications. Providence: American Mathematical Society, 433 s. Pure and applied undergraduate texts. ISBN 978-0-8218-4790-9. (EN)
Pratt, W. K.: Digital Image Processing (Second Edition), New York: Wiley 1991 (EN)
Recommended reading
Klíma, M.; Bernas, M.; Hozman J.; Dvořák, P. : Zpracování obrazové informace, , 0 (CS)
Elearning
Classification of course in study plans
- Programme N-MAI-P Master's 2 year of study, winter semester, compulsory
- Programme N-FIN-P Master's 2 year of study, winter semester, compulsory
- Programme N-PMO-P Master's 2 year of study, winter semester, compulsory-optional
- Programme C-AKR-P Lifelong learning
specialization CZS , 1 year of study, winter semester, elective
Type of course unit
Lecture
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
Teacher / Lecturer
Syllabus
1. Color spaces, histogram
2. Image visualization - pixel value transformation
3. Fourier transform of functions of two real variables and its properties
4. One-dimensional and two-dimensional discrete Fourier transform
5. Visualization of spectrum, its basic modifications
6. Convolution, image filtration, additive noise
7. Impulse noise
8. Analysis of directions in image
9. Image registration, phase correlation
10. Image compression, cosine transform, JPG
11. Image segmentation - thresholding, edge detection
12. Object analysis, moment method
13. Reserve of the lecturer, semestral project
Elearning