Automatic pigment identification from hyperspectral data

Grabowski, Bartosz; Masarczyk, Wojciech; Głomb, Przemysław; Mendys, Agata

doi:10.1016/j.culher.2018.01.003

Cited by 81 publications

(52 citation statements)

References 37 publications

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“…There have been many efforts in obtaining accurate pigment classification using spectral classification algorithms (Almeida et al 2013;Bacci et al 2007;Cosentino 2014;Delaney et al 2005;Grabowski et al 2018;Rohani et al 2016). Chemometric techniques (Baronti et al 1998) for classification of pigments were also investigated.…”

Section: Introductionmentioning

confidence: 99%

Spectral-divergence based pigment discrimination and mapping: A case study on The Scream (1893) by Edvard Munch

Deborah

George

Hardeberg

2019

Journal of the American Institute for Conservation

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An important application of imaging spectroscopy or hyperspectral imaging is the classification or discrimination of pigments based on the obtained spectral reflectance information. As opposed to being a point-analysis tool, this non-invasive method captures the entire surface of interest. This means that its potential is not only in the discrimination of pigments but also in their mapping. However, the challenge lies in the fact that in a real painting, there is no clear-cut edge between regions with certain pure pigments or of the exact same mixture. Pigments and other paint materials mix seamlessly and continuously in the physical domain. In this article, we introduce a divergence-based approach to pigment discrimination and mapping. The methodology is then applied to Munch's masterpiece The Scream (1893), whose pigments and materials have been identified for several points in the painting in a previous study. Through the introduced methodology, we have been able to extend the point analyzes of pigments and materials to the entire surface of the painting, recto and verso. RÉSUMÉUne importante application de la spectro-imagerie ou imagerie hyperspectrale est la classification ou la différenciation de pigments en fonction des données de réflectance spectrale obtenues. Contrairement à un instrument d'analyse ponctuel, cette méthode non invasive examine la surface d'intérêt dans son ensemble. Cela signifie que son potentiel n'est pas seulement la différenciation de pigments mais aussi leur cartographie. Cependant, la difficulté réside dans le fait que dans une véritable peinture, il n'y a pas de limite nette entre des zones de pigments purs ou de différents mélanges de ces pigments. Les pigments et autres matériaux constitutifs d'une peinture se mélangent imperceptiblement et continuellement dans le domaine physique. Dans cet article nous présentons une approche basée sur la divergence de spectre pour la différenciation des pigments et leur cartographie. Cette méthodologie est ensuite appliquée au chef-d'oeuvre de Munch Le Cri (1893), dont les pigments et matériaux constitutifs ont été identifiés en plusieurs points de la peinture dans une étude précédente. Grâce à la méthodologie proposée, nous avons pu étendre les analyses ponctuelles de pigments et autres matériaux à l'ensemble de la surface de la peinture, recto et verso. Traduit par Claire Cuyaubère. RESUMOUma aplicação importante da espectroscopia de imagem ou imagem hiperespectral é a classificação ou discriminação de pigmentos com base na informação de refletância espectral obtida. Ao contrário de ser uma ferramenta de análise pontual, esse método não invasivo captura toda a superfície de interesse. Isso significa que seu potencial não está apenas na discriminação de pigmentos, mas também em seu mapeamento. No entanto, o desafio reside no fato de que, em uma pintura real, não há uma borda nítida entre regiões com certos pigmentos puros ou que contenham exatamente a mesma mistura. Pigmentos e outros materiais de pintura se misturam perfeitamente e continuam...

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Section: Introductionmentioning

confidence: 99%

Spectral-divergence based pigment discrimination and mapping: A case study on The Scream (1893) by Edvard Munch

Deborah

George

Hardeberg

2019

Journal of the American Institute for Conservation

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“…In this work, a pixel‐wise unmixing problem is solved to produce semi‐quantitative maps of pigment distributions. We are inspired by recent work that developed a multilayer perceptron neural network for classifying pigments, as well as studies from the field of remote sensing in which convolutional neural networks, stacked auto‐encoders, deep belief networks, and restricted Boltzmann machines have been used for feature extraction, classification, and unmixing . In this study, pigments present in a given spectrum are identified using a deep neural network designed to perform supervised classification.…”

Section: Figurementioning

confidence: 99%

Nonlinear Unmixing of Hyperspectral Datasets for the Study of Painted Works of Art

et al. 2018

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Nonlinear unmixing of hyperspectral reflectance data is one of the key problems in quantitative imaging of painted works of art. The approach presented is to interrogate a hyperspectral image cube by first decomposing it into a set of reflectance curves representing pure basis pigments and second to estimate the scattering and absorption coefficients of each pigment in a given pixel to produce estimates of the component fractions. This two‐step algorithm uses a deep neural network to qualitatively identify the constituent pigments in any unknown spectrum and, based on the pigment(s) present and Kubelka–Munk theory to estimate the pigment concentration on a per‐pixel basis. Using hyperspectral data acquired on a set of mock‐up paintings and a well‐characterized illuminated folio from the 15th century, the performance of the proposed algorithm is demonstrated for pigment recognition and quantitative estimation of concentration.

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“…Spectral imaging is an emerging tool in analysis in the field of conservation and restoration of Cultural Heritage (CH) [1,2]. Some of the most relevant applications found are the identification of pigments and materials used in the original artworks [3,4], the characterization of the preservation state using spectral ranges beyond the visible [5] and the visualization of hidden layers or "pentimenti" using the infrared range [6]. However, few studies have focused on the use of spectral imaging on the ageing process of the materials commonly used in artworks or ancient texts [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spectral information to get beyond color in the analysis of water-soluble varnish degradation

et al. 2019

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Spectral images were captured of paper samples varnished with two water-soluble materials: gum arabic and egg white. The samples were submitted to degradation processes that partially or totally eliminated the varnish from the substrate (water immersion and ageing). The spectral information was used to obtain average color data and to characterize the spatial and color inhomogeneity across pixels, showing that the pixel spectral data are critical for an accurate characterization of the degradation process of the varnishes. Since the varnishes typically become yellower with ageing, this study introduces two novel and simple-to-compute yellowness indices based on the spectral information, which are validated against a standard colorimetric index (ASTM-E313 2015). The potential uses of spectral information are demonstrated with several pieces of a real antique map sample by comparing the spectral information measured before and after cleaning the sample. To sum up, the main contributions of this study are the characterization of the spatial homogeneity through pixel-based spectral and color information and the proposal of spectral-based yellowing indices for two critical applications (ageing process follow-up and effect of cleaning), as demonstrated with synthetic and historical samples of varnished paper respectively.

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Automatic pigment identification from hyperspectral data

Cited by 81 publications

References 37 publications

Spectral-divergence based pigment discrimination and mapping: A case study on The Scream (1893) by Edvard Munch

Spectral-divergence based pigment discrimination and mapping: A case study on The Scream (1893) by Edvard Munch

Nonlinear Unmixing of Hyperspectral Datasets for the Study of Painted Works of Art

Spectral information to get beyond color in the analysis of water-soluble varnish degradation

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