Processing of Multidimensional signals
FEKT-CZVSAcad. year: 2019/2020
The Processing of Multidimensional Signals course addresses one-dimensional time signals and two-dimensional image signals as well. Computer based methods and procedures intended for signal and image processing are the main parts of the course.
Learning outcomes of the course unit
Graduate of the course is able to design and to implement algorithms and methods for processing of both one-dimensional time signals and two-dimensional image signals.
The basic knowledge on the level of secondary school is required in the Processing of Multidimensional Signals course.
Recommended optional programme components
Recommended or required reading
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)
Russ J.C.: The Image Processing Handbook. CRC Press 1995. ISBN 0-8493-2516-1. (EN)
Planned learning activities and teaching methods
Teaching methods include lectures and computer exercises. Course is taking advantage of the e-learning (Midas) system.
Assesment methods and criteria linked to learning outcomes
Weekly computer exercises (40 pts) and a final exam (60 pts) are evaluated during the Processing of Multidimensional Signals course. For successful pass the course, obtaining of at least half of available points is required in both mentioned parts.
Language of instruction
1. Introduction to signal processing.
2. Introduction to image processing.
3. Discrete image.
4. Image representation and properties.
5. Brightness transformations.
6. Geometrical transformations.
7. Noise filtration.
8. Edge and corner detection.
9. Integral transform I.
10. Integral transform II.
11. Mathematical morphology.
12. Colour models.
13. Image files formats.
The course is divided into two parts: discrete signals and discrete images. First of all, fundamentals of signal processing, sampling theory, signal reconstruction and discrete filters are introduced with a view to further image processing. Second part of the course contains theory of discrete image processing as geometric and brightness transformations, integral transformations, gradient operators, mathematical morphology and fundamentals of segmentation and classification.
Specification of controlled education, way of implementation and compensation for absences
The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.