Standoff chemical imaging finds evidence for Jackson Pollock's selective use of alkyd and oil binding media in a famous ‘drip’ painting

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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Section: Figurementioning

confidence: 99%

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

et al. 2018

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“…This allows large areas to be scanned (1.7 × 1.4 m 2 ). This scanner has been used to collect XRF spectra and make elemental maps of several paintings [5][6][7][8][9], although this work was done with a lower power X-ray source having a 65-micrometer spot size that has been replaced as described here. Since the original development of the Gallery's XRF scanner, a variety of noncommercial, XRF scanners for cultural heritage science have been developed based on laboratory X-ray sources.…”

Section: Introductionmentioning

confidence: 99%

“…The decision to co-collect reflectance spectra is based on a long demonstrated synergy between XRF, which provides elemental information, and reflectance spectroscopy, which provides molecular information (i.e. electronic transitions and absorption features associated with vibrational modes) (1,(5)(6)(7). In these studies a registration took place between the data collected from the XRF scanner and independently obtained reflectance image cubes collected using high efficiency (640-1000 spatial pixels along the slit) and high spatial sampling hyperspectral cameras (0.16-0.3 mm 2 /pixel).…”

Section: Introductionmentioning

confidence: 99%

Integrated X-ray fluorescence and diffuse visible-to-near-infrared reflectance scanner for standoff elemental and molecular spectroscopic imaging of paints and works on paper

et al. 2018

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Prior studies have shown the improved ability to identify artists' pigments by combining results from X-ray fluorescence (XRF), which provides elemental information, with reflectance spectroscopy in the visible to near infrared (400-1000 nm) that provides information on electronic transitions. Extending the spectral range of reflectance spectroscopy into the UV, 350-400 nm, allows identification of several white pigments since their electronic transitions occur in this region (e.g., zinc white and rutile and anatase forms of titanium white). Extending the range further into the infrared, out to 2500 nm, provides information on vibrational transitions of various functional groups, such as hydroxyl, carbonate, and methyl groups. This allows better identification of mineral-based pigments and some paint binders. The combination of elemental information with electronic and vibrational transitions provides a more robust method to identify artists' materials in situ. The collection of both sets of spectral information across works of art, such as paintings and works on paper, allows generating a more complete map of artists' materials. Here, we describe a 2-D scanner that simultaneously collects XRF spectra and reflectance spectra from 350 to 2500 nm across the surfaces of works of art. The scanner consists of a stationary, single pixel XRF spectrometer and fiber optic reflectance spectrometer along with a 2-D position-controlled easel that moves the artwork in front of the two detection systems. The dualmode scanner has been tested on a variety of works of art from illuminated manuscripts (0.1 × 0.1 m 2) to paintings as large as 1.7 × 1.9 m 2. The scanner is described and two sets of results are presented. The first is the XRF scanning of a large warped panel painting by Andrea del Sarto titled Charity. The second is a combined XRF and reflectance scan of Georges Seurat's painting titled Haymakers at Montfermeil. The XRF was collected at 1 mm spatial sampling and the reflectance spectral data at 3 mm. Combining the results from the data sets was found to enhance the identification of pigments as well as yield distribution maps, in spite of the relatively low reflectance spatial sampling. The elemental and reflectance maps allowed the identification and mapping of lead white, cobalt blue, viridian, ochres, and likely chrome yellow. The maps also provide information on the mixing of pigments. While the reflectance image cube has 10-20× larger spatial samples than desired, the elimination of having to use two hyperspectral cameras to cover the range from 400 to 2500 nm makes for a low cost dual modality scanner.

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“…Pigment identification allows for an understanding of how artists/workshops used their materials,h ow painted surfaces may chemically change over time,and, lastly,how anachronistic uses of materials can be associated with either fakes/ forgeries or past restorations.Aprimary tool for these tasks is hyperspectral imaging (HSI), af ast non-invasive and in situ method that has become commonplace in cultural heritage to document the distribution of pigments across painted surfaces,e specially when employed together with complementary analytical techniques. [1][2][3][4][5][6][7][8][9][10][11] Pigment mixture identification from HSI datasets is an ongoing challenge.I tusually combines non-automated data interpretation (namely,asimple visual comparison to literature reflectance data) and classification algorithms.Mapping pigments with HSI datasets is achieved by linear spectral unmixing methods,f or example,m aximum-likelihood estimation, spectral angle mapping, subspace projection methods, or constrained least-squares.T hese approaches consider the spectrum as al inear combination of two or more endmembers,d efined as as pectrum representing ap ure pigment. While linear unmixing is computationally fast and easy to implement, pigment mixtures have an on-linear reflectance response.…”

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confidence: 99%

“…(2) in the Supporting Information, section B] provides an estimation of pigment concentrations.T he KM solver is used in combination with ac onstrained nonlinear multivariable optimization function that minimizes the reconstruction error between the experimental spectrum and reconstructed one [Eq. (10) in the Supporting Information, section E].T he minimization is subject to two constraints on the concentration values,that is,abundances need to sum to one and abundances are non-negative (more details about gradient descent method are given in Refs. [34,35] and the Supporting Information, section E).…”

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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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Standoff chemical imaging finds evidence for Jackson Pollock's selective use of alkyd and oil binding media in a famous ‘drip’ painting

Abstract: Near-infrared diffuse reflectance imaging spectroscopy (NIR-RIS, 1000 to 2500 nm) was used to map the use of alkyd and oil paints in Jackson Pollock'sNumber 1, 1950(Lavender Mist), one of his most important ‘drip’ or ‘poured’ paintings.

Cited by 26 publications

References 17 publications

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

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

Integrated X-ray fluorescence and diffuse visible-to-near-infrared reflectance scanner for standoff elemental and molecular spectroscopic imaging of paints and works on paper

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

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