January 5, 2024 Dataset Open Access

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

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    <subfield code="a">Fernando García-Avello Bofías</subfield>
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    <subfield code="a">Caterina Bracci</subfield>
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    <subfield code="a">Michele Ginolfi</subfield>
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    <subfield code="a">Chiara Ruberto</subfield>
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    <subfield code="a">&lt;p&gt;Synthetic Datasets for ICSC Flagship 2.6.1. &amp;quot;Fast Extended Computer Vision&amp;quot;&amp;nbsp; paper #1 &amp;quot;Datacube segmentation via Deep Spectral Clustering&amp;quot;&lt;/p&gt;

&lt;p&gt;It is a preliminary paper for the ICSC Spoke 2 WP6 flagship 2.6.1 &amp;quot;Fast Extended Computer Vision&amp;quot;.&lt;/p&gt;

&lt;p&gt;Code repository at: https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster&lt;/p&gt;

&lt;p&gt;Abstract:&lt;/p&gt;

&lt;blockquote&gt;
&lt;p&gt;Extended Vision techniques are a ubiquitous in physics. However, the&amp;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.&lt;br&gt;
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&amp;#39;s spectra, performed in a suitably defined low-dimensional embedding space.&lt;br&gt;
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.&lt;br&gt;
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.&lt;/p&gt;
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    <subfield code="a">Alessandro Bombini</subfield>
    <subfield code="u">INFN, Firenze &amp; ICSC</subfield>
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    <subfield code="a">artificial intelligence</subfield>
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    <subfield code="a">Variational Autoencoder</subfield>
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    <subfield code="a">This work is (partially) supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – 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 – 4CH) and by the project AIRES–CH - Artificial Intelligence for digital REStoration of Cultural Heritage jointly funded by the Tuscany Region (Progetto Giovani Sì) and INFN.</subfield>
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    <subfield code="a">Synthetic Datasets for ICSC Flagship 2.6.1. "Extended Computer Vision at high rate" paper #1 "Datacube segmentation via Deep Spectral Clustering"</subfield>
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