January 5, 2024 Other Open Access

Alessandro Bombini; Fernando García-Avello Bofías; Caterina Bracci; Michele Ginolfi; Chiara Ruberto

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  <dc:creator>Alessandro Bombini</dc:creator>
  <dc:creator>Fernando García-Avello Bofías</dc:creator>
  <dc:creator>Caterina Bracci</dc:creator>
  <dc:creator>Michele Ginolfi</dc:creator>
  <dc:creator>Chiara Ruberto</dc:creator>
  <dc:date>2024-01-05</dc:date>
  <dc:description>Trained models for the paper "Datacube segmentation via Deep Spectral Clustering".
It is a preliminary paper for the ICSC Spoke 2 WP6 flagship 2.6.1 "Fast Extended Computer Vision".

Code repository at: https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster

Abstract:


Extended Vision techniques are a ubiquitous in physics. However, the  data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube.
Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outiline some essential properties of the case study at hand, e.g.~it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space.
To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e.~perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by an iterative K-Means clustering algorithm.
We apply this technique on two different use cases, of different physical origin: a set of MA-XRF data on pictorial artworks, and a synthetic dataset of simualted astrophysical observations.
</dc:description>
  <dc:identifier>https://www.openaccessrepository.it/record/143543</dc:identifier>
  <dc:identifier>10.15161/oar.it/143543</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster</dc:relation>
  <dc:relation>doi:10.15161/oar.it/143545</dc:relation>
  <dc:relation>doi:10.15161/oar.it/143542</dc:relation>
  <dc:relation>url:https://www.openaccessrepository.it/communities/infn</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:subject>artificial intelligence</dc:subject>
  <dc:subject>neural networks</dc:subject>
  <dc:subject>Variational Autoencoder</dc:subject>
  <dc:subject>Deep Clustering</dc:subject>
  <dc:subject>Trained Models</dc:subject>
  <dc:subject>Flagship 2.6.1</dc:subject>
  <dc:subject>ICSC Spoke 2</dc:subject>
  <dc:title>Trained Models for ICSC Flagship 2.6.1. "Extended Computer Vision at high rate" paper #1 "Datacube segmentation via Deep Spectral Clustering"</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>other</dc:type>
</oai_dc:dc>