16 February 2010 Analytical imaging of cultural heritage paintings using digitally archived images
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Abstract
Interests in cultural heritage have grown tremendously in the past few years. These interests vary from preservation, restoration, inspection and archiving just to name a few. Access to cultural heritage is very limited. Therefore, it is important to maximize such opportunity to gather as much information as possible from the cultural heritage. In this study, a technique was proposed to extract analytical information from digitally archived images. The images were acquired at high resolution using a flatbed scanner equipped with a line CCD under fluorescent light illumination. The images were used to reconstruct spectral reflectance using the pseudoinverse method. The results were used for pigment identification and investigation on degradation. Three methods were explored in computing the conversion matrix which contains information from the light source and the camera based on over 600 Japanese pigments as learning samples: (1) use of all pigments in the database; (2) exclusion of some pigments if historical information is available on the sample; and (3) color classification using L*C*H* color space. The technique was applied to the analysis of a real cultural heritage, a hanging scroll painting called Dragon King Zennyo Ryu'o (classified as a Japanese National Treasure, dated 1145) found in Koya Mountain in Japan. The analytical information extracted from the archived images provided insights on the degradation process the painting underwent. In addition, the traces of material detected from the analysis, give art historians scientific proof in creating historical footprints for this precious cultural artifact. This study demonstrated how archived RGB images could be used for the noninvasive and nondestructive investigation of actual cultural heritage.
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Jay Arre Toque, Yuji Sakatoku, Ari Ide-Ektessabi, "Analytical imaging of cultural heritage paintings using digitally archived images", Proc. SPIE 7531, Computer Vision and Image Analysis of Art, 75310N (16 February 2010); doi: 10.1117/12.839995; https://doi.org/10.1117/12.839995
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