January 5, 2024 Dataset Open Access

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

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  "@id": "https://doi.org/10.15161/oar.it/143545", 
  "@type": "Dataset", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0001-7225-3355", 
      "@type": "Person", 
      "affiliation": "INFN, Firenze & ICSC", 
      "name": "Alessandro Bombini"
    }, 
    {
      "@id": "https://orcid.org/0000-0001-6640-8736", 
      "@type": "Person", 
      "affiliation": "INFN, Firenze", 
      "name": "Fernando Garc\u00eda-Avello Bof\u00edas"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Universit\u00e0 di Firenze & INAF, Firenze", 
      "name": "Caterina Bracci"
    }, 
    {
      "@id": "https://orcid.org/0000-0002-9122-1700", 
      "@type": "Person", 
      "affiliation": "Universit\u00e0 di Firenze & INAF, Firenze", 
      "name": "Michele Ginolfi"
    }, 
    {
      "@id": "https://orcid.org/0000-0003-0321-7160", 
      "@type": "Person", 
      "affiliation": "Universit\u00e0 di Firenze & INFN, Firenze", 
      "name": "Chiara Ruberto"
    }
  ], 
  "datePublished": "2024-01-05", 
  "description": "<p>Synthetic Datasets for ICSC Flagship 2.6.1. &quot;Fast Extended Computer Vision&quot;&nbsp; paper #1 &quot;Datacube segmentation via Deep Spectral Clustering&quot;</p>\n\n<p>It is a preliminary paper for the ICSC Spoke 2 WP6 flagship 2.6.1 &quot;Fast Extended Computer Vision&quot;.</p>\n\n<p>Code repository at: https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster</p>\n\n<p>Abstract:</p>\n\n<blockquote>\n<p>Extended Vision techniques are a ubiquitous in physics. However, the&nbsp; 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.<br>\nFurthermore, 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&#39;s spectra, performed in a suitably defined low-dimensional embedding space.<br>\nTo 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.<br>\nWe 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.</p>\n</blockquote>", 
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      "@type": "DataDownload", 
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  "identifier": "https://doi.org/10.15161/oar.it/143545", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "artificial intelligence", 
    "neural networks", 
    "Variational Autoencoder", 
    "Deep Clustering", 
    "Trained Models", 
    "Flagship 2.6.1", 
    "ICSC Spoke 2"
  ], 
  "license": "https://creativecommons.org/licenses/by/4.0/", 
  "name": "Synthetic Datasets for ICSC Flagship 2.6.1. \"Extended Computer Vision at high rate\" paper #1 \"Datacube segmentation via Deep Spectral Clustering\"", 
  "url": "https://www.openaccessrepository.it/record/143545", 
  "version": "20240105"
}