Colour and Space in Cultural Heritage: Key Questions in 3D Optical Documentation of Material Culture for Conservation, Study and Preservation

Boochs, Frank; Bentkowska-Kafel, Anna; Degrigny, Christian; Karaszewski, Maciej; Karmacharya, Ashish; Kató, Zoltán; Picollo, Marcello; Sitnik, Robert; Trémeau, Alain; Tsiafaki, Despoina; Tamás, Levente

doi:10.1007/978-3-319-13695-0_2

Cited by 10 publications

(8 citation statements)

References 3 publications

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“…Colorimetric measurements can be used effectively to analyze color objects and their changes over time, monitoring the fading or the damage of the pigments by light. For this purpose, calculating color differences between neighboring areas is recommended in order to provide information about color degradation [11,21]. Color fading MFT observations on-site are difficult to set up (hostile sites to be reached, rough panels, narrow ground, no electricity, etc.)…”

Section: Introductionmentioning

confidence: 99%

Color degradation mapping of rock art paintings using microfading spectrometry

Carrión-Ruiz

Riutort-Mayol

Molada-Tebar

et al. 2021

Journal of Cultural Heritage

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Section: Introductionmentioning

confidence: 99%

Color degradation mapping of rock art paintings using microfading spectrometry

Carrión-Ruiz

Riutort-Mayol

Molada-Tebar

et al. 2021

Journal of Cultural Heritage

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“…Precise color recording is a priority, yet not trivial, task in heritage documentation [14,15]. Traditional methodologies for color description in archaeology are mostly based on visual observations which are strictly subjective.…”

Section: Introductionmentioning

confidence: 99%

Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation

et al. 2020

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The camera characterization procedure has been recognized as a convenient methodology to correct color recordings in cultural heritage documentation and preservation tasks. Instead of using a whole color checker as a training sample set, in this paper, we introduce a novel framework named the Patch Adaptive Selection with K-Means (P-ASK) to extract a subset of dominant colors from a digital image and automatically identify their corresponding chips in the color chart used as characterizing colorimetric reference. We tested the methodology on a set of rock art painting images captured with a number of digital cameras. The characterization approach based on the P-ASK framework allows the reduction of the training sample size and a better color adjustment to the chromatic range of the input scene. In addition, the computing time required for model training is less than in the regular approach with all color chips, and obtained average color differences Δ E a b * lower than two CIELAB units. Furthermore, the graphic and numeric results obtained for the characterized images are encouraging and confirms that the P-ASK framework based on the K-means algorithm is suitable for automatic patch selection for camera characterization purposes.

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“…The appropriate description of color is not a trivial issue in cultural heritage documentation [3,4]. Color is a matter of perception, which largely depends on the subjectivity of the observer.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Molada-Tebar

Riutort-Mayol

Marqués-Mateu

et al. 2019

Sensors

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In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and sans-serifΔEab* color differences. Values of less than 3 CIELAB units were achieved for sans-serifΔEab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and sans-serifΔEab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

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Colour and Space in Cultural Heritage: Key Questions in 3D Optical Documentation of Material Culture for Conservation, Study and Preservation

Cited by 10 publications

References 3 publications

Color degradation mapping of rock art paintings using microfading spectrometry

Color degradation mapping of rock art paintings using microfading spectrometry

Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

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