Analysis of Signals and Images
FEKT-LASOAcad. year: 2015/2016
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.
Learning outcomes of the course unit
The graduate is capable of:
- 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.
The subject knowledge on the Bachelor´s degree level is requested, particularly mathematics and digital signal processing
Recommended optional programme components
Recommended or required reading
G.Strang, T.Nguyen: Wavelts and Filter Banks. Wellesley-Cambridge Press, 1996
J.Jan: Číslicová filtrace, analýza a restaurace signálů. VUTIUM 2002
J.Jan: Medical Image Processing, Reconstruction and Restoration. CRC 2006
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. Techning methods include lectures and computer laboratories, and namely selfstudy. Course is taking advantage of e-learning (Moodle) system. Students have to write projects/assignments during the course.
Assesment methods and criteria linked to learning outcomes
Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
- 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)
Language of instruction
1. Time-frequency signal analysis, wavelet transforms.
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.
The goal of the course is to provide the students with knowledge of time-frequency signal analysis and particularly of digital signal processing and analysis.
Specification of controlled education, way of implementation and compensation for absences
Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
- obligatory computer-lab tutorial
- voluntary lecture
Classification of course in study plans
- Programme EEKR-ML Master's
branch ML-BEI , 1. year of study, winter semester, 6 credits, compulsory
- Programme EEKR-ML1 Master's
branch ML1-BEI , 1. year of study, winter semester, 5 credits, compulsory
- Programme EEKR-CZV lifelong learning
branch ET-CZV , 1. year of study, winter semester, 5 credits, compulsory