Ink recognition based on statistical classification methods

Kokla, Vassiliki; Psarrou, Alexandra; Konstantinou, Vassilis

doi:10.1109/dial.2006.24

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Mohammadi and Berns [2006] show that it is crucial to capture the full KM coefficients for a pigment in order to accurately reproduce the range of generated colors. Kokla et al [2006] propose a way to use a set of captured pigments to determine from a simple image of a painting or manuscript the mixture of pigments used for each local color, via an optimization.…”

Section: Pigment Acquisitionmentioning

confidence: 99%

RealPigment

DiVerdi

Chen

et al. 2014

Proceedings of the Workshop on Non-Photorealistic Animation and Rendering

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a) color chart c John Perry (b) alpha blending (c) radial basis functions (d) Kubelka-Munk Figure 1: Color compositing: (a) a color chart crafted in marker in order to observe how different pigments appear when layered; (b) alpha blending, though standard in computer graphics, fails to capture known effects like yellow over blue gives green; (c) radial basis functions interpolate and extrapolate from the data in the chart to produce more realistic effects; (d) fitting parameters of a Kubelka-Munk model to the data in the chart improves on radial basis functions for paint-like behaviors. AbstractThe color of composited pigments in digital painting is generally computed one of two ways: either alpha blending in RGB, or the Kubelka-Munk equation (KM). The former fails to reproduce paint like appearances, while the latter is difficult to use. We present a data-driven pigment model that reproduces arbitrary compositing behavior by interpolating sparse samples in a high dimensional space. The input is an of a color chart, which provides the composition samples. We propose two different prediction algorithms, one doing simple interpolation using radial basis functions (RBF), and another that trains a parametric model based on the KM equation to compute novel values. We show that RBF is able to reproduce arbitrary compositing behaviors, even non-paint-like such as additive blending, while KM compositing is more robust to acquisition noise and can generalize results over a broader range of values.

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Section: Pigment Acquisitionmentioning

confidence: 99%

RealPigment

DiVerdi

Chen

et al. 2014

Proceedings of the Workshop on Non-Photorealistic Animation and Rendering

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A New Perspective on Material Classification and Ink Identification

Shiradkar¹,

Shen

Landon

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Contemporary approach in the study of a Byzantine-illuminated manuscript on parchment

Kokla

2021

Eur. Phys. J. Plus

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The aim of this study was the application of image-based techniques on the texts and painted elements of a manuscript whose date of manufacture is attributable to the twelfth century. The application of the proposed methodology resulted in documentation of the ink and pigment types used, the change on writings, the repainting on some old initial letters with more artistic initials of later times, as well as, later changes on the illuminations of the manuscript. A large quantity of information was recovered applying imaging techniques in different wavelength bands, which was combined with the historical and paleographical knowledge, while some of knowledge was evaluated using targeted scanning electron microscopy with energy-dispersive X-ray (SEM/EDX) analysis of chosen inks and pigments. Furthermore, computational visual analysis was applied on the multispectral images in order to investigate the range of black inks used on an entire page of the manuscript. In this way, it is possible to identify all inks found on all pages of the manuscript. Thus, the imaging techniques can play a crucial role in the study of materials on a low budget, without expensive analytical techniques being required. The proposed methodology provided important information, not only on the characterization of the manufacture materials but on their preservation conditions, as well. The documented preservation status of the inks and pigments allowed the application of more precise conservation treatments under full control.

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Ink recognition based on statistical classification methods

Abstract: Statistical classification methods can be applied to images of historical manuscripts in order to

Cited by 3 publications

References 10 publications

RealPigment

RealPigment

A New Perspective on Material Classification and Ink Identification

Contemporary approach in the study of a Byzantine-illuminated manuscript on parchment

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