wooden panel
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2021 ◽  
Vol 21 (2) ◽  
pp. 2_109-2_129
Author(s):  
Hisamitsu KAJIKAWA ◽  
Yuka OKADA ◽  
Ryotaro SHIRAI
Keyword(s):  

2020 ◽  
Vol 1 (2) ◽  
pp. 48-65
Author(s):  
Nesrin El Hadidi ◽  
Hamdy Abdel-monem ◽  
Mourad Mohamed ◽  
Ghada Hashem

2020 ◽  
Vol 34 ◽  
pp. 335-342
Author(s):  
Maria Tonca ◽  
Mioara Mihaela Sîntiuan

"The Restoration of the icon on wood “Our Lady of the Sign” The paper describes the state of conservation of a Lipovan icon, with the theme “Our Lady of the Sign”, presenting the degradation forms, and also describes the restoration stages, characterized by specific techniques and materials. The icon dates from the beginning of 19th century and it is painted in tempera technique on wooden panel. It is one of the oldest Russian Orthodox Mariological Iconography celebrated on November 27. Keywords: conservation, restoration, icon, wooden panel, ”Our Lady of the Sign” "


2020 ◽  
Vol 46 ◽  
pp. 165-175
Author(s):  
Jean-Christophe Dupre ◽  
Delphine Jullien ◽  
Luca Uzielli ◽  
Franck Hesser ◽  
Lorenzo Riparbelli ◽  
...  

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mostafa Attia Mohie ◽  
Gilan Mahmoud Sultan

Purpose This paper aims to provide a deeper understanding of the painting techniques, materials used and deterioration phenomena in a thin panel painting. As well as, straightening buckling in a thin panel painting and reinforcement have been used by an auxiliary support system. Design/methodology/approach This requires using several scientific and analytical techniques to provide a deeper understanding of the painting techniques, materials used, deterioration phenomena and a greater awareness of how well treatment the panel painting is. Visual observation and multispectral imaging (Visible Ultraviolet-induced luminescence, as well as Ultraviolet reflected and Infrared [IR]), optical Microscopy (OM), handheld X-ray fluorescence spectroscopy (XRF), X-ray diffraction, Fourier transform IR spectroscopy (FTIR) and gas chromatography were used in this case study. Findings The analytical study of a thin panel with different methods allowed defining that the thin panel painting consists of plywood panel, ground layer (white lead and animal glue) and painted layer (lead red, cobaltic black, chrome yellow, Venetian red, iron black and white lead and poppy oil). Also, these determined that a convex buckling was the main form of deterioration. The structure treatment was executed by using a wet compress to straighten the thin panel painting and followed by fixing a new special design of the second auxiliary support system on the back of the thin panel painting. Originality/value The importance of analytical study to determine the painting techniques, materials used, deterioration phenomena and how well treatment the panel painting is. As well as, using a wet compress to straightening of warping or buckling wooden panel painting. Also, the Plexiglas second auxiliary support system could use to reinforcement the wooden panel and control the wooden panel movements.


Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2101 ◽  
Author(s):  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Ramona Quattrini ◽  
Marco Mameli ◽  
Emanuele Frontoni

In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are—the three “Ideal Cities” (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the “Studiolo del Duca” with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80 %), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums’ visitors.


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