Course detail

Processing of multidimensional signals

FEKT-BZVSAcad. year: 2014/2015

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.

Language of instruction

Czech

Number of ECTS credits

6

Mode of study

Not applicable.

Guarantor

Ing. Karel Horák, Ph.D.

Department

Department of Control and Instrumentation (UAMT)

Learning outcomes of the course unit

An absolvent 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.

Prerequisites

The basic knowledge on the level of secondary school is required in the Processing of Multidimensional Signals course.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods include lectures and computer exercises. Course is taking advantage of e-learning (Midas) system.

Assesment methods and criteria linked to learning outcomes

Weekly computer exercises (10x4 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.

Course curriculum

1. Introduction to Signal Processing.
2. Discrete Image.
3. Image Acquisition.
4. Brightness Transformation.
5. Geometric Transformation.
6. Integral Transformation.
7. Edge and Corner Detection.
8. Noise Filtering.
9. Image Segmentation.
10. Description.
11. Classification.
12. Mathematical Morphology.

Work placements

Not applicable.

Aims

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Hlaváč V., Šonka M.: Počítačové vidění. Grada 1992. ISBN 80-85424-67-3. (CS)
Russ J.C.: The Image Processing Handbook. CRC Press 1995. ISBN 0-8493-2516-1. (EN)
Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision. Thomson 2008. ISBN 978-0-495-08252-1. (EN)

Classification of course in study plans

  • Programme EECC Bc. Bachelor's

    branch B-AMT , 3 year of study, winter semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Karel Horák, Ph.D.

Exercise in computer lab

39 hod., compulsory

Teacher / Lecturer

Ing. Karel Horák, Ph.D.
Ing. Jan Klečka, Ph.D.