|Teoria||4||I semestre||Gloria Menegaz|
|Laboratorio||2||I semestre||Gloria Menegaz|
The course aims at providing the theoretical and practical knowledge for the time(space)/frequency analysis of multidimensional signals. The mail focus will be on the Wavelet Transform as well as on applications in the ICT framework.
At the end of the course the student will be able to identify the best approach to process signals, images and volumetric data depending on the problem at hand such as feature extraction for pattern recognition, compression and coding, multiscale representation of multidimensional signals.
The student will be able to design and implement a signal processing system using different computational platforms including Matlab and Python as well as analyse the performance of the algorithm.
At the end of the course the student will be able i) to propose solutions to a given problem from both the theoretical and algorithmic point of view; ii) to learn new mathematical tools for the representation, analysis and processing of multidimensional signals; iii) to implement the algorithm on the most suitable platform.
1) Background: overview of mathematical tools
- Fourier transform for signals and images
- Windowed Fourier Transform
2) Wavelets and multiresolution representations
- Introduction to multiresolution theory
- Wavelet bases
- Wavelet families and overcomplete representations
- Fast Wavelet Transform (DWT)
- Extension to multiple dimensions
- Applications in ICT and biomedical frameworks
The exam consists in an oral and a project. The oral will concern multiresolution theory. The project will be assigned during the lab. sessions.
|Teoria||Stephane Mallat||A Wavelet Tour of Signal Processing (Edizione 2)||Academic Press||1999||9780124666061|