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

Additional information

Suitable for postgraduate studies.

Person responsible

Alessandro Foi

Lessons

Implementation Period Person responsible Requirements
SGN-25006 2017-01 3 Alessandro Foi

Learning Outcomes

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

Instructions for students on how to achieve the learning outcomes

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.

Assessment scale:

Numerical evaluation scale (0-5)

Study material

Type Name Author ISBN URL Additional information Examination material
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   

Correspondence of content

There is no equivalence with any other courses

Updated by: Foi Alessandro, 22.02.2018