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首页> 外文期刊>Journal of spectroscopy >Pigment Identification of Ancient Wall Paintings Based on a Visible Spectral Image
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Pigment Identification of Ancient Wall Paintings Based on a Visible Spectral Image

机译：基于可见光谱图像的古壁绘画颜料鉴定

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Many ancient wall paintings are confronted with the threat of irreversible damages and in urgent requirement of restoration. This work provides the superpixel segmentation method and pigment identification method for the visible spectral image of ancient wall paintings to guide the scientific restoration of the paintings. The superpixel segmentation method for the visible spectral image is an extension of SLIC (Simple Linear Iterative Clustering) for the RGB image by redefining the feature of the visible spectral image. It can extract the outline of wall paintings and limit the pigment filling area in restoration of wall paintings. 44 kinds of commonly used pigments with size variations are selected to construct a visible spectral reference database for pigment identification. The pigment used in each superpixel is identified by searching the database in a specifically constructed feature space to find the nearest reference sample. This can provide guidance to pigment selection in restoration of wall paintings. At last, the methods are validated using the visible spectral image captured from Mogao Grottoes in Dunhuang by using a multispectral imaging system.
机译：许多古老的墙绘画面临着不可逆转损害的威胁和迫切要求的恢复。这项工作为古墙绘画的可见光谱图像提供了Superpixel分段方法和颜料识别方法，以指导绘画的科学恢复。可见光谱图像的SuperPixel分割方法是通过重定定义可见光谱图像的特征来为RGB图像的SLIC（简单线性迭代聚类）的延伸。它可以提取壁画的轮廓，并限制颜料灌装区域恢复壁画。选择具有尺寸变化的44种常用颜料以构建可见光谱参考数据库，用于颜料鉴定。通过在专门构造的特征空间中搜索数据库来识别每个超像素中使用的颜料以找到最近的参考样本。这可以为墙绘的恢复恢复的颜料选择提供指导。最后，通过使用多光谱成像系统，使用从敦煌在敦煌的可见光谱图像验证了该方法。

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