Course detail

Analysis of Signals and Images

FEKT-MASOAcad. year: 2010/2011

Time-frequency signal analysis. Continuous and discrete image representation, 2D transforms, stochastic image. Enhancement and edition of images - contrast transforms, sharpening, noise and interference suppression, geometric operations. Introduction to restoration of distorted images. Methods of image reconstruction from parallel and fan tomographic projections. Non-linear analysis and filtering of signals and images, neuronal classifiers. Edge, border and area detection, image segmentation. Analysis and visualisation of 2D and 3D image data. Technical, medical and ecological applications.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

prof. Ing. Jiří Jan, CSc.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

Good overview on methods of image (and signal) processing and analysis, ability perform practical applications

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested, particularly elements of digital signal processing

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

written exam

Course curriculum

1. Time-frequency signal analysis.
2. Continuous and discrete image representation, 2D transforms, stochastic image.
3. Enhancement and edition of images - contrast transforms, sharpening, noise and interference suppression, geometric operations.
4. Introduction to restoration of distorted images.
5. Methods of image reconstruction from parallel and fan tomographic projections.
6.Non-linear analysis and filtering of signals and images, neuronal classifiers.
7.Edge, border and area detection, image segmentation. Analysis and visualisation of 2D and 3D image data.
8.Technical, medical and ecological applications.

Work placements

Not applicable.

Aims

Gaining knowledge on time-frequency signal analysis and particularly on digital signal processing and analysis

Specification of controlled education, way of implementation and compensation for absences

computer lab

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Jan, J.: Medical Image Processing, Reconstruction and Restoration, CRC Press 2005
J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000,

Recommended reading

A.K.Jain:Fundamentals of Digital Image Processing, Prentice Hall Int. Edit., 1989
A.Rosenfeld, A.C.Kak:Digital Picture Processing (2nd ed.), Acad. Press 1982
Banks, S., Signal Processing, Image Processing and Pattern Recognition. Prentice Hall Int. (UK) Ltd., 1990
Gonzales, R.C. , Wintz, P.: Digital Image Processing. 2nd ed. Addison-Wesley Publ.Comp. 1987
W.K.Pratt:Digital Image Processing (2nd ed.),J.Wiley 1992

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-KAM , 2 year of study, winter semester, elective interdisciplinary
    branch M-EST , 1 year of study, winter semester, elective interdisciplinary
    branch M-BEI , 1 year of study, winter semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, compulsory

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

prof. Ing. Jiří Jan, CSc.

Syllabus

Time-frequency signal analysis, wavelet transforms
Continuous image representation, linear 2D systems, 2D spectra
Discrete image representation, discrete 2D linear operators, separable and convolutory local operators
Discrete 2D transforms: DFT, cosine, sine, Hadamard, Haar transforms
Image enhancement, edition and geometric operations
Image noise and interference suppression
Elements of formalised image restoration, pseudoinverse filtering
Tomographic image reconstructions from projections - principles of algebraic methods, of methods via frequency domain and by filtered back-projection
Non-linear analysis of signals and images - homomorphic and median filtering
Elements of signal/image analysis and filtering by neural networks
Image segmentation, edge, boarder and area detection
Movement- and depth analysis. Visualisation of 3D and 4D image data.
Applications of image analysis in technology, medicine and ecology.

Exercise in computer lab

26 hod., compulsory

Teacher / Lecturer

Ing. Petr Walek, Ph.D.
Ing. Martin Lamoš, Ph.D.

Syllabus

Becoming acquainted with MATLAB - Image Processing Toolbox environment
Wavelet analysis of complicated signals
Experimental acquisition of image data. Basic operations with image data in original domain
2D discrete systems, verification of characteristics
Generating of discrete stochastic fields, correlation analysis
2D DFT, image spectra
Contrast end colour enhancement, histogram equalisation
Image sharpening and interference suppression
Distortion (or blurr) identification, design and verification of modified inverse filtering
Experimental Radon transform and aproximative reconstruction from projections based on spectral slice theorem
Aproximative reconstruction from projections by filtered back projection
Basic methods of image segmentation, texture analysis
Manipulation with image data in common compressed formats