Artworks in the spotlight: characterization with a multispectral LED dome

Ciortan, Irina Mihaela; Dulecha, Tinsae Gebrechristos; Giachetti, Andrea; Pintus, Ruggero; Villanueva, Alberto Jaspe; Gobbetti, Enrico

doi:10.1088/1757-899x/364/1/012025

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…The coins have been acquired with a multi-spectral light dome capturing the reflectance in the visible, IR, and UV domains. 15 Images have been pre-processed and corrected to account for the non-uniformity of the light beams using the pipeline described in. 15 Three or four coins have been acquired for each scan, placed at a distance of approximately 5cm over a dark, matte background.…”

Section: Application: Relighting and Analysis Of Roman Coinsmentioning

confidence: 99%

“…15 Images have been pre-processed and corrected to account for the non-uniformity of the light beams using the pipeline described in. 15 Three or four coins have been acquired for each scan, placed at a distance of approximately 5cm over a dark, matte background. In this configuration the variation in the light directions within a coin region is small and the directional light assumption reasonable.…”

Section: Application: Relighting and Analysis Of Roman Coinsmentioning

confidence: 99%

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Ancient coins' surface inspection with web-based neural RTI visualization

Righetto,

Traviglia,

De Bernardin

et al. 2023

Optics for Arts, Architecture, and Archaeology (O3A) IX

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The use of neural encodings has the potential to replace the commonly used polynomial fitting in the analysis of artwork surface based on Reflectance Transformation Imaging (RTI), as it has proved to result in more compact encoding with better relight quality, but it is still not widely used due to the lack of efficient implementations available to practitioners.In this work, we describe an optimized system to encode/decode neural relightable images providing interactive visualization in a web interface allowing multi-layer visualization and annotation. To develop it, we performed several experiments testing different decoder architectures and input processing pipelines, evaluating the quality of the results on specific benchmarks to find the optimal tradeoff between relighting quality and efficiency. A specific decoder has been then implemented for the web and integrated into an advanced visualisation tool. The system has been tested for the analysis of a group of ancient Roman bronze coins that present scarce readability and varying levels of preservation and that have been acquired with a multispectral light dome. Their level of corrosion and degradation, which in some cases hinders the recognition of the images, numerals, or text represented on them, makes the system testing particularly challenging and complex. Testing on such a real case scenario, however, enables us to determine the actual improvement that this new RTI visualization tool can offer to numismatists in their ability to identify the coins.

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Section: Application: Relighting and Analysis Of Roman Coinsmentioning

confidence: 99%

Section: Application: Relighting and Analysis Of Roman Coinsmentioning

confidence: 99%

Ancient coins' surface inspection with web-based neural RTI visualization

Righetto,

Traviglia,

De Bernardin

et al. 2023

Optics for Arts, Architecture, and Archaeology (O3A) IX

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“…The two fluorescent mockups that contain impressions of convex spherical objects as well as concave patterns were acquired with a multispectral RTI light dome, 4 with lights distributed at different elevation (50 • , 30 • and 10 • ) and azimuth angles. The ambient light was controlled by covering the dome in black non-reflective clothing.…”

Section: Rti and Fti Data Acquisitionmentioning

confidence: 99%

“…At the same time, Reflectance Transformation Imaging (RTI) has become an established multi-light image technique used for 2.5D reconstruction, appearance modelling and relighting with numerous active applications in the Cultural Heritage community. [1][2][3][4][5] The methods designed under the RTI umbrella have been tested for opaque and diffuse objects, as well as opaque and shiny surfaces. Even though there are a few works on using RTI for the rendering of translucent 6 and fluorescent materials, 7 the literature is still scarce on multi-light imagery of special materials.…”

Section: Introductionmentioning

confidence: 99%

Fluorescence transformation imaging

Ciortan

Giachetti

George

et al. 2021

Optics for Arts, Architecture, and Archaeology VIII

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Fluorescent or luminescent materials absorb light at shorter wavelengths and re-emit at longer wavelengths. In terms of appearance, this translates to a increased color vividness, as well as a glowing effect. Thanks to these properties, the study of fluorescent materials is of interest for Cultural Heritage applications, where fluorophores are incorporated in many pigments and varnishes. This paper proposes a variant to Reflectance Transformation Imaging (RTI) technique, namely Fluorescence Transformation Imaging (FTI), that handles luminescent objects. Reflectance Transformation Imaging method outputs a single-camera multi-light image collection of a static scene, which can be used to model the reflectance of the scene as a polynomial of the illumination directions. Similarly, Fluorescence Transformation Imaging aims to model the fluorescent signal based on a series of images with fixed scene and viewpoint and varying incident light directions -what changes with respect to RTI is that fluorescence is recorded instead of reflected radiation. In the literature, there are works that explore the isotropic property of fluorescence in low-dimension multi-light imagery methods (such as Photometric Stereo) to model the appearance of an object with a first-order polynomial. This is based on the assumption that in the fluorescent mode the object gets closer to a Lambertian surface than in the reflective mode where non-Lambertian effects such as highlights are more likely to appear. Nonetheless, this assumption stands for single-object scenes, with uniform albedo and convex geometries. When there are multiple fluorescent objects in the scene, with concavities and non-uniform fluorescent component, then the fluorescence can become secondary light to the object and become a source of interreflections. Through quantitative and qualitative analysis, this paper explores the Reflectance and Fluorescence Transformation Imaging methods and the resulting texture maps towards appearance rendering of heterogeneous non-flat fluorescent objects.

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“…Multi-directional illumination image fusion enhancement method, which fusing a set of images of a scene acquired with a fixed viewpoint and a varying set of lighting conditions, can provide large amounts of shape and appearance information of the target object nondestructively, and can be treated as a substitute of 3D scanner in various fields where precisely structural dimensions are not that critical, such as feature detection and augmentation [25,26], shape and appearance reconstruction [27], appearance extraction [28,29]. One of the most classic ways is the socalled albedo visualization which is a light-independent view of the constant spatially varying bidirectional reflectance distribution function across the surface [30,31]. Since it removes the high-frequency component that might disturb the underlying content from the signal, fine surface details that are usually contained in this part would be completely lost that they cannot be directly applied to text recognition.…”

Section: Introductionmentioning

confidence: 99%

Metallic debossed characters industrial online non‐segmentation identification based on improved multi‐scale image fusion enhancement and deep neural network

Zhong

Zhou

2021

IET Image Processing

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This paper investigates accurate industry online scene text recognition techniques for metallic debossed characters (MDCs). As MDCs have low contrast with its background and easy to interfere with corrosion rust, oxidate skin or specular reflection and so forth, a multi-scale image fusion algorithm is first proposed to restore the MDCs' shape and appearance information to enhance the sample's contrast by using the MDCs' depth characteristic with the help of four-directional illumination. With this method, the three most important reflection-related picture quality evaluation standards, namely contrast, saturation, and exposure, can be enhanced simultaneously. Next, based on the fusion result, a U-shaped network based on VGG-16 network architecture is used for text localization. In order to improve network receptive fields, an improved post-processing output module that combines a text sequence score map for coarsening localization with a single-character score map for fine localization is proposed, so that the network's adaptability and accuracy for long texts are improved. Finally, a convolutional recurrent neural network is adopted to realize the recognition of sequence characters. The advantages and effectiveness of the proposed method are statistically analyzed with the data from a liquefied petroleum gas cylinder annual periodic inspection production line.

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Artworks in the spotlight: characterization with a multispectral LED dome

Cited by 4 publications

References 2 publications

Ancient coins' surface inspection with web-based neural RTI visualization

Ancient coins' surface inspection with web-based neural RTI visualization

Fluorescence transformation imaging

Metallic debossed characters industrial online non‐segmentation identification based on improved multi‐scale image fusion enhancement and deep neural network

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