Course unit, curriculum year 2026–2027
COMP.SGN.230
Vector Space Methods for Signal and Image Processing, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 3 (1.1.2027–7.3.2027)
Active in period 4 (8.3.2027–31.5.2027)
Course code
COMP.SGN.230Language of learning
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 %
Content
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 conditions: Attendance of at least 80% of the lecture hours. Successful completion 80% of the home assignments. Pass the Exam.
All parts of the completion option are compulsory.
Participation in teaching
Contact teaching
11.01.2027 – 26.02.2027
Active in period 3 (1.1.2027–7.3.2027)