Evidence map›Paper›PMID 34577356›Full record

ArticleSensors (Basel, Switzerland)2021

Artificial Intelligence for Pigment Classification Task in the Short-Wave Infrared Range.

Emeline Pouyet, Tsveta Miteva, Neda Rohani, Laurence de Viguerie

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
16.3field-weighted citation impact, top 1% of its field
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

5 citing papers in PubMed, 23 citations in OpenAlex.

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  4. Review
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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

4 authors at 2 institutions in 2 countries.

Emeline PouyetLaboratoire d'Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France.ORCID 0000-0001-5824-2940
Tsveta MitevaLaboratoire de Chimie Physique-Matière et Rayonnement (LCPMR), UMR 7614, CNRS, Sorbonne Université, 75005 Paris, France.ORCID 0000-0003-1701-4348
Neda RohaniMicrosoft, Bellevue, WA 98004, USA.
Laurence de ViguerieLaboratoire d'Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France.
Centre National de la Recherche Scientifique · FRMicrosoft (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, "extended NIR", ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

Indexed as

Artificial IntelligenceNeural Networks, ComputerDiagnostic ImagingPigmentationRadio Wavesdeep neural networkhyperspectral imaging in the short-wave infrared rangepigment mappingreflectance imaging spectroscopythangkas

Identifiers

PMID34577356
PMCPMC8471921
OpenAlexW3199040267

What OpenQuestion holds

Textmetadata
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.