January 5, 2024 Dataset Open Access

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

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        "affiliation": "INFN, Firenze & ICSC", 
        "name": "Alessandro Bombini", 
        "orcid": "0000-0001-7225-3355"
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      {
        "affiliation": "INFN, Firenze", 
        "name": "Fernando Garc\u00eda-Avello Bof\u00edas", 
        "orcid": "0000-0001-6640-8736"
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      {
        "affiliation": "Universit\u00e0 di Firenze & INAF, Firenze", 
        "name": "Caterina Bracci"
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        "affiliation": "Universit\u00e0 di Firenze & INAF, Firenze", 
        "name": "Michele Ginolfi", 
        "orcid": "0000-0002-9122-1700"
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        "affiliation": "Universit\u00e0 di Firenze & INFN, Firenze", 
        "name": "Chiara Ruberto", 
        "orcid": "0000-0003-0321-7160"
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    "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>", 
    "doi": "10.15161/oar.it/143545", 
    "keywords": [
      "artificial intelligence", 
      "neural networks", 
      "Variational Autoencoder", 
      "Deep Clustering", 
      "Trained Models", 
      "Flagship 2.6.1", 
      "ICSC Spoke 2"
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    "language": "eng", 
    "license": {
      "id": "CC-BY-4.0"
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    "notes": "This work is (partially) supported by ICSC \u2013 Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union \u2013 NextGenerationEU, by the European Commission within the Framework Programme Horizon 2020 with the project \"4CH - Competence Centre for the Conservation of Cultural Heritage\"  (GA n.101004468 \u2013 4CH) and by the project AIRES\u2013CH - Artificial Intelligence for digital REStoration of Cultural Heritage jointly funded by the Tuscany Region (Progetto Giovani S\u00ec) and INFN.", 
    "publication_date": "2024-01-05", 
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    "title": "Synthetic Datasets for ICSC Flagship 2.6.1. \"Extended Computer Vision at high rate\" paper #1 \"Datacube segmentation via Deep Spectral Clustering\"", 
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