Difference between revisions of "Fernando/SignalAnalysisPhDCourse"
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'''Signal Analysis in 1D''' | '''Signal Analysis in 1D''' | ||
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Discrete-time signals | Discrete-time signals | ||
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'''Signal Analysis in 2D''' | '''Signal Analysis in 2D''' | ||
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Directionality analysis | Directionality analysis | ||
* Structure tensor, HOGs, Gabor | * Structure tensor, HOGs, Gabor | ||
* Edges, corners | * Edges, corners | ||
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Feature analysis | Feature analysis | ||
* Segmentation, clustering | * Segmentation, clustering | ||
* Feature extraction, pattern matching and classification | * Feature extraction, pattern matching and classification | ||
Revision as of 21:28, 10 November 2016
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| Multiscale and Multidimensional Analysis | |
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| Contact: Fernando Alonso-Fernandez |
Contents
Multiscale and Multidimensional Analysis course (Autumn 2016)
This is the web page for the PhD course Multiscale and Multidimensional Analysis.
Course information
Instructor: Fernando Alonso-Fernandez
Class time: The schedule for the course will be made ad hoc, using doodle or by agreement over email.
Class location: F5.
Office hours: by appoinment.
Contact: feralo(AHTT)hh.se.
Course description
The course is focused on the topic of multi-scale and multi-dimensional signal analysis. More particularly, it will provide with advanced concepts and techniques to extract useful information from signals of arbitrary dimension (such as audio (1D) and image (2D) signals), drawing on topics from the signal/image processing and computer vision fields.
Communication: by email or in person. Communication WILL NOT be done through blackboard.
Office hours: There are no regularly scheduled office hours, but you can always arrange a meeting with the instructors. Just send an email or drop by.
Grading: by assignment
Prerequisites: linear algebra (vector and matrix operations), probability theory/statistics, signal processing, image processing and multi-variate calculus. The course assumes a programing background (primarily in Matlab).
Course content
Signal Analysis in 1D
Discrete-time signals
- Types and properties of signals
- Sampling of signals
Discrete-time systems
- Time-domain analysis: LTI systems, properties, convolution
- Frequency-domain analysis: DFT, other orthogonal basis/wavelets, filters
Signal Analysis in 2D
Directionality analysis
- Structure tensor, HOGs, Gabor
- Edges, corners
Feature analysis
- Segmentation, clustering
- Feature extraction, pattern matching and classification
