The multispectral imaging of the Archimedes Palimpsest

Easton, Roger L.; Noel, William

doi:10.3406/galim.2004.1646

Cited by 15 publications

(10 citation statements)

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“…With respect to the source estimates, we considered the following normalized leastsquares error (NLSE) for each couple of original and estimated sources (31) where and are the original and the estimated th source, respectively. With respect to the matrix estimate, we defined two quality indexes: 1) the root mean squared error (rmse) between the original and estimated matrices and 2) the element-by-element ratio between the two matrices.…”

Section: Resultsmentioning

confidence: 99%

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A Markov model for blind image separation by a mean-field EM algorithm

Tonazzini

Bedini

Salerno

2006

IEEE Trans. on Image Process.

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Abstract-This paper deals with blind separation of images from noisy linear mixtures with unknown coefficients, formulated as a Bayesian estimation problem. This is a flexible framework, where any kind of prior knowledge about the source images and the mixing matrix can be accounted for. In particular, we describe local correlation within the individual images through the use of Markov random field (MRF) image models. These are naturally suited to express the joint pdf of the sources in a factorized form, so that the statistical independence requirements of most independent component analysis approaches to blind source separation are retained. Our model also includes edge variables to preserve intensity discontinuities. MRF models have been proved to be very efficient in many visual reconstruction problems, such as blind image restoration, and allow separation and edge detection to be performed simultaneously. We propose an expectation-maximization algorithm with the mean field approximation to derive a procedure for estimating the mixing matrix, the sources, and their edge maps. We tested this procedure on both synthetic and real images, in the fully blind case (i.e., no prior information on mixing is exploited) and found that a source model accounting for local autocorrelation is able to increase robustness against noise, even space variant. Furthermore, when the model closely fits the source characteristics, independence is no longer a strict requirement, and cross-correlated sources can be separated, as well.Index Terms-Blind source separation (BSS), edge and feature detection, Markov random fields (MRFs), parameter learning, scene analysis.

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Section: Resultsmentioning

confidence: 99%

“…8(e). In particular, this one simulates the colors and the mutual position of the two overlapped texts as they appear in the famous Archimedes palimpsest [31]. Note that the underwriting is the lighter text.…”

Section: Resultsmentioning

confidence: 99%

A Markov model for blind image separation by a mean-field EM algorithm

Tonazzini

Bedini

Salerno

2006

IEEE Trans. on Image Process.

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“…Currently, MSI and HSI analysis are routinely employed to study pictorial and graphic features [77] and for the recovery of the original contrast between pigments and supports in damaged parchment manuscripts [78]. In particular, recent studies have demonstrated the potentiality of UVF in enhancing the readability of faded features in manuscripts [79].…”

Section: Damaged Surface Decorationmentioning

confidence: 99%

Pulsed Thermography Applied to the Study of Cultural Heritage

et al. 2017

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Abstract:In this paper, an overview of the recent applications of pulsed infrared thermography is presented. Pulsed infrared thermography, which provides stratigraphic information by analyzing the heat diffusion process within the sample after a thermal perturbation, is applied to the investigation of different kinds of cultural heritage artefacts. In particular, it is used to analyze repairs, decorative elements, and casting faults on bronzes, to detect texts hidden or damaged in ancient books/documents, and to characterize paint decorations. Moreover, the integration of pulsed infrared thermography and three-dimensional shape recording methods is proposed in order to provide a three-dimensional representation of the thermographic results. Finally, it is shown how the obtained thermographic results may be crucial from the historical and artistic points of view for understanding the modus operandi of a specific artist and/or of a workshop and for reconstructing the manufacturing process of the analyzed artefacts.

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“…MS imaging was originally developed for remote sensing applications and has recently found its way to the field of conservation [15]. In addition to its applicability for art conservation [16], the imaging technique has also proven effective for the analysis of historical and degraded documents [17]. The different spectral response of PCB boards is described in Kleber et al [18].…”

Section: Sensor Evaluationmentioning

confidence: 99%

Optical Sensor Evaluation for Vision Based Recognition of Electronics Waste

Kleber¹,

Pramerdorfer²,

Wetzinger³

et al. 2015

IJESD

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Abstract-For the online and offline classification of printed circuit boards and SMD components mounted on the board a sensor evaluation of optical sensors with different characteristics is presented. In overall a high resolution multispectral camera, a DSLR, an IP camera, and the RGB sensor of the Asus Xtion Pro Live have been evaluated for 2D images. For the 3D acquisition the Asus Xtion Pro Live is compared to the Bluetechnix M100 time of flight sensor. The sensor analysis shows the affected recognition performance of printed circuit boards and SMD components. Based on the sensor comparison, a pilot plant for the recycling of electronics waste is presented.

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The multispectral imaging of the Archimedes Palimpsest

Cited by 15 publications

References 0 publications

A Markov model for blind image separation by a mean-field EM algorithm

A Markov model for blind image separation by a mean-field EM algorithm

Pulsed Thermography Applied to the Study of Cultural Heritage

Optical Sensor Evaluation for Vision Based Recognition of Electronics Waste

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