Course detail
Analysis of Signals and Images
FEKT-LASOAcad. year: 2016/2017
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
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- being oriented in theoretical principles of signal and image analysis methods, and also in practical aspects of their implementation,
- designing suitable approaches and also provide consultations in this respect,
- aplying the respective programmes including commercial software and also of programming independently designed related algorithms,
- being a valid member of intedisciplinary teams in the area of signal and namely image analysis.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
basically:
- obtaining at least 12 points (out of 24 as course-unit credit based on active presence in demonstration exercises),
- successful passing of final written exam (up to 76 points)
Course curriculum
2. Continuous image representation, 2D transforms, stochastic image.
3. Discrete and digital image representation, 2D discrete transforms, discrete operators.
4. Enhancement and editing of images – contrast and colour scale transforms.
5. Mask operators, sharpening, noise suppression, geometric operations.
6. Introduction to restoration of distorted images
7. Local parameters, texture analysis and parametric image.
8. Image segmentation based on homogeneity, region oriented segmentation.
9. Image segmentation based on edge representation, Hough transform.
10. Image segmentation by the watershed method. Segmentation by flexible contours and level sets.
11. Generalised morphological transforms.
12. Reconstruction methods of images from parallel and fan projections, in original and spectral domain.
13 Nonlinear analysis and filtering of images, neuronal classifiers.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Basically:
- obligatory computer-lab tutorial
- voluntary lecture
Recommended optional programme components
Prerequisites and corequisites
Basic literature
J.Jan: Medical Image Processing, Reconstruction and Restoration. CRC 2006
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
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
Teacher / Lecturer
Syllabus
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