{
  "abstract": "The study of ancient materials is marked by their heterogeneity, uniqueness, and poor prior knowledge. Their analysis must be extensive, non-destructive, and precise. For these reasons, X-Ray Fluorescence (XRF) imaging is widely used for elemental analysis. X-Rays exposure time must balance extensiveness and sample preservation, a trade-off highly dependent on data exploitation: sufficient data must be collected for relevance without damaging the sample.\n\nTo address this, this article extends the work of Celeux et al. ( 2022) on spatially constrained hierarchical clustering of spectral images. It focuses on the probabilistic model characterizing XRF, deriving a dissimilarity and an associated cost function for comparing spectra. Tested within the aforementioned algorithm, it improves segmentation robustness to noise and relevance over the previously used χ 2 distance. Our results suggest equivalent material characterisation could be achieved with reduced exposure time, minimizing damage and enabling the collection of larger images.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Maxime Megel"
    },
    {
      "name": "Gilles Celeux"
    },
    {
      "name": "Serge X. Cohen"
    },
    {
      "name": "Agnes Grimaud"
    },
    {
      "name": "Clara Hairie"
    },
    {
      "name": "Christine Keribin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f66h3m7a23ya5.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": null,
  "id": "f66h3m7a23ya5",
  "issns": [],
  "landing_url": "https://hal.science/hal-05799747v2",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.math"
    ],
    [
      "hal",
      "1.info.info-lg"
    ],
    [
      "hal",
      "1.info.info-ts"
    ],
    [
      "hal",
      "1.math.math-st"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://hal.science/hal-05799747/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f66h3m7a23ya5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "hal-05799747",
      "source": "hal",
      "url": "https://hal.science/hal-05799747v2"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:1.math.math-st",
      "subfield": "statistics"
    }
  ],
  "title": "Hierarchical clustering of X-Ray Fluorescence images using a dissimilarity based on the likelihood ratio statistic",
  "title_text": "Hierarchical clustering of X-Ray Fluorescence images using a dissimilarity based on the likelihood ratio statistic",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/f66h3m7a23ya5",
  "venue": "HAL",
  "versions": []
}