Evidence map›Paper›PMID 33153178›Full record

ArticleSensors (Basel, Switzerland)2020

Digital Cultural Heritage Preservation in Art Painting: A Surface Roughness Approach to the Brush Strokes.

Anna Mironova, Frederic Robache, Raphael Deltombe, Robin Guibert, Ludovic Nys, Maxence Bigerelle

Abstract readLetter
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anna MironovaCNRS UMR 8201-LAMIH-Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines, University Polytechnique Hauts-de-France, F-59313 Valenciennes, France.
Frederic RobacheCNRS UMR 8201-LAMIH-Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines, University Polytechnique Hauts-de-France, F-59313 Valenciennes, France.
Raphael DeltombeCNRS UMR 8201-LAMIH-Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines, University Polytechnique Hauts-de-France, F-59313 Valenciennes, France.
Robin GuibertCNRS UMR 8201-LAMIH-Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines, University Polytechnique Hauts-de-France, F-59313 Valenciennes, France.
Ludovic NysCRISS-Laboratoire "Centre de Recherche Interdisciplinaire en Sciences de la Société", Bâtiment des Tertiales, University Polytechnique Hauts-de-France, Rue des Cent Têtes, F-59300 Valenciennes, France.
Maxence BigerelleCNRS UMR 8201-LAMIH-Laboratoire d'Automatique, de Mécanique et d'Informatique Industrielles et Humaines, University Polytechnique Hauts-de-France, F-59313 Valenciennes, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a growing interest in cultural heritage preservation. The notion of HyperHeritage highlights the creation of new means of communication for the perception and data processing in cultural heritage. This article presents the Digital Surface HyperHeritage approach, an academic project to identify the topography of art painting surfaces at the scale at which the elementary information of sensorial rendering is contained. High-resolution roughness and imaging measurement tools are then required. The high-resolution digital model of painted surfaces provides a solid foundation for artwork-related information and is a source of many potential opportunities in the fields of identification, conservation, and restoration. It can facilitate the determination of the operations used by the artist in the creative process and allow art historians to define, for instance, the meaning, provenance, or authorship of a masterpiece. The Digital Surface HyperHeritage approach also includes the development of a database for archiving and sharing the topographic signature of a painting.

Indexed as

art paintingcultural heritagedigitizationsurface roughness

Identifiers

PMID33153178
PMCPMC7663586

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.