Arduino-controlled Reflectance Transformation Imaging to the study of cultural heritage objects

Corregidor, V.; Dias, Renato Dutra; Catarino, N.; Cruz, Carlos Oliveira; Cruz, Jader

doi:10.1007/s42452-020-03343-4

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Some studies focus on surface normal and change detection of the angular deviation of the surface normal before and after an imposed change considering the nearest pixel. Corregidor et al extended edge detection analysis to RTI data for documenting defected areas on coins [18] based on documenting the visualisation by employing specular enhancement on two phases of data. Manfredi et al proposed a methodology that compares the topographic changes using the surface normal to characterise the change's directionality on a mockup painting before and after damage [19,20].…”

Section: Open Accessmentioning

confidence: 99%

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Supervised segmentation of RTI appearance attributes for change detection on cultural heritage surfaces

et al. 2022

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This paper proposes a supervised segmentation method for detecting surface changes based on appearance attributes, focusing on cultural heritage metal surfaces. Reflectance Transformation Imaging (RTI) reconstruction coefficients (PTM and HSH) are explored for tracking changes over time on different data sets. Each acquisition is normalised to ensure the method’s robustness, allowing consecutive acquisitions with different RTI acquisition parameters. The proposed method requires expert labelling on groups of pixels representing individual classes. Afterward, the surface appearance is identified over time based on the estimated discriminant model. After segmentation, each detected category is assigned to a single colour to present the results with a user-friendly colourmap visualisation. The method is user-dependent; the labelling of the pixels must be accurately defined based on the research question. The results were evaluated based on human expertise in the conservation-restoration field and are considered ground truth in this work. A case study with visibly segmentable characteristics was used to prove the concept and evaluate the invariance of the proposed method. Comparison with the segmentation of the visible characteristics shows very accurate segmentation for HSH (99%) and lower for PTM (80%), which is influenced by surface rotation. The method was tested on metal surfaces undergoing accelerated corrosion or cleaning treatments. The results were promising for tracking changes based on segmentation. Equally promising is the possibility of qualitative quantifying the degree of change by counting the change of a selected class of pixels. PTM and HSH results are comparable in cases of mat surfaces; however, in high specular surfaces, HSH seems to provide more detailed information and, therefore, can better depict the surface characteristics. Limitations of the application are related to the possibility of identifying surface characteristics that do not exhibit topographic changes or significant reflectance differentiation.

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Section: Open Accessmentioning

confidence: 99%

“…The conservation-restoration expert selected three classes in each case based on the surface mentioned above characteristics (Figs. 17,18).…”

Section: Monitoring Cleaning Treatmentmentioning

confidence: 99%

Supervised segmentation of RTI appearance attributes for change detection on cultural heritage surfaces

et al. 2022

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“…Existing RTI instruments are often too bulky or heavy for in situ use, or designed for laboratory use. Recently, some researchers developed devices to overcome this limit, especially by designing open-source devices [22][23][24]. Ahmadi and Hubbard [25] developed an open-source device composed of the microscopic camera and a lighting half-sphere for the study of millimeter-sized objects.…”

Section: Introductionmentioning

confidence: 99%

A New RTI Portable Instrument for Surface Morphological Characterization

Lemesle,

Bigerelle

2024

Hardware

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A new instrument using reflectance transformation imaging (RTI), named MorphoLight, has been developed for surface characterization. This instrument is designed to be adjustable to surfaces, ergonomic, and uses a combination of high-resolution imaging functions, i.e., focus stacking (FS) and high dynamic range (HDR), to improve the image quality. A topographical analysis method is proposed with the instrument. This method is an improvement of the surface gradient characterization by light reflectance (SGCLR) method. This aims to analyze slope/curvature maps, traditionally studied in RTI, but also to find the most relevant lighting position and 3D surface parameter which highlight morphological signatures on surfaces and/or discriminate surfaces. RTI measurements and analyses are performed on two zones, sky and sea, of a naval painting which have the same color palette but different painting strokes. From the statistical analysis using bootstrapping and analysis of variance (ANOVA), it is highlighted that the high-resolution images (stacked and tonemapped from HDR images) improve the image quality and make it possible to better see a difference between both painting zones. This difference is highlighted by the fractal dimension for a lighting position (θ, φ) = (30°, 225°); the fractal dimension of the sea part is higher because of the presence of larger brushstrokes and painting heaps.

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“…RTI has also been explored as a scientific tool in the past decade. Different perspectives have been examined for documenting not only object surfaces but also conservation treatments [13][14][15][16][17][18] or for revealing underlying information with the integrated use of spectral imaging [15,19] and to map defects with clear topographic changes [20,21]. However, little research exists for documenting metal surfaces [22] or monitoring metal conservation treatments [13,17,18].…”

Section: Introductionmentioning

confidence: 99%

Reflectance Transformation Imaging (RTI) Data Analysis for Change Detection: Application to Monitoring Protective Coating Failure on Low Carbon Steel

Siatou,

Nurit,

Saha

et al. 2023

archiving

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This paper examines two new methodological approaches exploring Reflectance Transformation Imaging (RTI) data processing for detecting, documenting, and tracking surface changes. The first approach is unsupervised and applies perpixel calculations on the raw image stack to extract information related to specific surface attributes (angular reflectance, microgeometry). The second method proposes a supervised segmentation approach that, based on machine learning algorithms, uses coefficients of a fitting model to separate the surface's characteristics and assign them to a class. Both methodologies were applied to monitor coating failure, in the form of filiform corrosion, on low carbon steel test samples, mimicking treated historical metal objects' surfaces. The results demonstrate the feasibility of creating accurate cartographies that depict the surface characteristics and their location. Additionally, they provide a qualitative evaluation of corrosion progression that allows tracking and monitoring changes on challenging surfaces.

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Arduino-controlled Reflectance Transformation Imaging to the study of cultural heritage objects

Cited by 7 publications

References 24 publications

Supervised segmentation of RTI appearance attributes for change detection on cultural heritage surfaces

Supervised segmentation of RTI appearance attributes for change detection on cultural heritage surfaces

A New RTI Portable Instrument for Surface Morphological Characterization

Reflectance Transformation Imaging (RTI) Data Analysis for Change Detection: Application to Monitoring Protective Coating Failure on Low Carbon Steel

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