Course unit, curriculum year 2024–2025
COMP.SGN.230
Vector Space Methods for Signal and Image Processing, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 3 (1.1.2025–2.3.2025)
Active in period 4 (3.3.2025–31.5.2025)
Course code
COMP.SGN.230Language of instruction
EnglishAcademic years
2024–2025, 2025–2026, 2026–2027Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Alessandro FoiResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
Course Content:
- Vector and inner-product spaces
- Bases, Analysis and Synthesis
- Orthonormalization, Approximations
- Continuous-variable models for discrete data
- Global vs Local Processing
- Windowing and weighted norms
- Moving least squares
- Frames and Dual frames
- Singular Value Decomposition, Principal components and KLT transforms
- Smoothing operators
- Discrete differential operators
- Color spaces and color and multispectral image processing
- Popular transforms
- Noise propagation
All the above is explained and demonstrated through a vast set of working examples in Matlab and Python.
Learning outcomes
Further information
Learning material
Studies that include this course
Completion option 1
Pass of examination is required.
Completion of all options is required.
Participation in teaching
07.01.2025 – 24.02.2025
Active in period 3 (1.1.2025–2.3.2025)