Course detail
Fundamentals of Image Processing
FEKT-MPA-FIPAcad. year: 2025/2026
At the beginning of the course, students will get acquainted with the theory of digitization of image data, their computer representation and data formats. The following is a description of the function of 2D operators for linear and nonlinear filtering of images with specific examples of their use. Subsequently, students will get acquainted with the basic methods of pattern and object recognition in images, image segmentation, object tracking, principles of stereoscopy and reconstruction of 3D objects. Finally, the basic principles of modern methods using machine learning (neural networks, deep learning for regression, classification and segmentation) will be explained.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
Rules for evaluation and completion of the course
The definition of controlled teaching and the method of its implementation is determined by the annually updated decree of the subject guarantor.
Aims
The graduate of the course is able to: (a) explain the principle and procedure of image digitalization, (b) explain the principles of 2D digital systems, (c) perform basic digital image processing operations, (d) analyze the basic properties and information contained in digital images, (e) to orientate in the application of modern methods of image processing and analysis using machine learning.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
JAN, J. Digital signal filtering, analysis and restoration. London: Institution of Electrical Engineers, 2000. IEE telecommunications series, 44. ISBN 978-085-2967-607. (EN)
WALEK, P., LAMOŠ, M a JAN, J. Analýza biomedicínských obrazů: počítačová cvičení. 2. Brno: Vysoké učení technické v Brně, FEKT, ÚBMI, 2015. ISBN 978-80-214-4792-9. (CS)
Classification of course in study plans
- Programme MPA-AEE Master's 1 year of study, winter semester, compulsory-optional
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
1. Basics of signal representation of images and their spectra
2. Digital images and digital operators
3. Basic methods of modifying images with point operators
4. Basic methods of image editing with local operators in the spatial and frequency domain
5. Parametric images and texture analysis
6. Methods for detecting and tracking objects in images
7. Image segmentation methods
8. Geometric transformations of images
9. Stereoscopy and its use for distance estimation
10. Methods of 3D object reconstruction using stereoscopy and multiscopy
11. Machine learning methods for classification and regression
12. Principles of deep learning methods and convolutional neural networks
13. Architectures and applications of deep learning methods in autonomous driving
Exercise in computer lab
Teacher / Lecturer
Syllabus
1. Basics of signal representation of images and their spectra
2. Digital images and digital operators
3. Basic methods of modifying images with point operators
4. Basic methods of image editing with local operators in the spatial and frequency domain
5. Parametric images and texture analysis
6. Methods for detecting and tracking objects in images
7. Image segmentation methods
8. Geometric transformations of images
9. Stereoscopy and its use for distance estimation
10. Methods of 3D object reconstruction using stereoscopy and multiscopy
11. Machine learning methods for classification and regression
12. Principles of deep learning methods and convolutional neural networks
13. Architectures and applications of deep learning methods in autonomous driving