SGN-25006 Vector Space Methods for Signal and Image Processing, 5 cr


Suitable for postgraduate studies.


Alessandro Foi


After completing this course, the student will - be able to model and solve signal processing problems through a wide range of vector-space methods - be able to extend the geometrical intuition from familiar 2D and 3D to very high-dimensional settings within a rigorous framework - be able to implement a wide range of fixed and adaptive signal transforms and understand their general properties - master the geometrical structures inherent to basic signal processing techniques, including color transforms, convolution filters, sliding window methods. - be able to design and implement consistent multi-dimensional differential operators for discrete signals - be analyze the propagation of signal distortions (e.g., noise) across transformations

Ohjeita opiskelijalle osaamisen tasojen saavuttamiseksi

Measures of learning: Exam and mandatory Matlab exercises/mini-project. The grade is determined by the exam and execution of the Matlab exercises/mini-project.


Numerical evaluation scale (0-5)


Tyyppi Nimi Tekijä ISBN URL Lisätiedot Tenttimateriaali
Book   A wavelet tour of signal processing: the sparse way   S. Mallat         No   
Book   Frames for undergraduates   D. Han et al.         No   
Journal   A unified framework for bases, frames, subspace bases, and subspace frames   G. Farnebäck         No   
Lecture slides   slides and software examples from the lectures   A. Foi         Yes   


Opintojakso ei vastaan mitään toista opintojaksoa

Päivittäjä: Kunnari Jaana, 05.03.2019