The module will provide a brief introduction to classical pattern recognition for signal/timeseries and for images. We will cover approaches working on the spatial (temporal) and frequency (spectral) domain, presenting methods to represent temporal and visual information in static descriptors, as well as approaches to identify relevant patterns in the data (feature descriptors). Methodologies covered include correlation analysis, Fourier analysis, wavelets, intensity gradient-based descriptors and detectors, normalized cut segmentation.
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Date |
Topic |
References
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Additional Material
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2
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21/02/2024
(16-18)
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Signal processing
Timeseries; time domain analysis (statistics, correlation); spectral analysis; fourier analysis.
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| 3 |
22/02/2024
(14-16) |
Image Processing I
Spatial feature descriptors (color histograms, SIFT); spectral analysis.
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Additional readings
[1] Survey on visual descriptors
Software:
- A tweakable and fast implementation of SIFT in C (on top of OpenCV)
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27/02/2024
28/02/2024
29/02/2024
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LECTURES CANCELLED (WILL BE RECOVERED) |
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| 4 |
01/03/2024
(14-16) Room L1
(Recovery Lecture)
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Image Processing II
Feature detectors (edge, blobs); image segmentation; wavelet decompositions
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Additional readings
[2] Survey on visual feature detectors
A
reference book for the pattern recognition part is " S. THEODORIDIS, K.
KOUTROUMBAS, Pattern Recognition, 4th edition". It is not needed for
the sake of the course, but it is a reference book if you are interested
on the topic. It is not available online for free (legally; what you do
with Google is none of my business).
You can find the original NCUT paper freely available from authors here.
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