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Trained Models for ICSC Flagship 2.6.1. "Extended Computer Vision at high rate" paper #1 "Datacube segmentation via Deep Spectral Clustering"

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

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


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.

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.
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