中国激光, 2018, 45 (1): 0104001, 网络出版: 2018-01-24
基于光学相干层析成像的古代瓷器釉层分类 下载: 805次
Microstructures of Ancient Porcelains Based on Optical Coherence Tomography
测量 光学相干层析成像 图像纹理分析 主成分分析 清凉寺窑址 measurement optical coherence tomography image texture analysis principal component analysis Qingliangsi site
摘要
为探索基于光学相干层析成像(OCT)技术对古代青瓷釉层物理结构的分类, 综合应用OCT技术、X射线荧光光谱分析(XRF)技术、扫描电子显微镜-能谱分析(SEM-EDS)技术和激光拉曼光谱(LRS)技术对河南省宝丰县清凉寺窑址出土的金元时期青瓷和钧瓷样品残片进行了分析。根据获取的样品釉层物理结构OCT灰度图像特征对釉层进行定性分类, 利用图像纹理分析技术对釉层OCT图像进行量化表征, 并根据所确定的纹理特征参数进行主成分分析。对根据OCT图像对瓷釉的分类结果与根据XRF获得的釉层化学成分的分类结果进行比较, 结合SEM-EDS和LRS分析结果讨论了釉层材料学特征与OCT图像特征之间的内在联系。
Abstract
In order to study the classification of ancient porcelains based on the physical structures of glaze layer, optical coherence tomography (OCT) technology combined with X-ray fluorescence spectroscopy (XRF) technology, scanning electron microscope-energy dispersive spectroscopy (SEM-EDS) technology and laser Raman spectroscopy (LRS) technology are used to analyze the Celadon and Jun porcelains which are dated to the Jin and Yuan dynasties and excavated from the Qingliangsi site in Baofeng County, Henan Province, China. The glaze layers are classified qualitatively according to the characteristics of OCT images of glaze layers. The image texture analysis technique is used to characterize the OCT images of glaze layers quantitatively, and principal component analysis is performed based on the image texture parameters determined by the image texture analysis. The classification results of the enamel according to the OCT image are compared with the classification results of the chemical composition of the glaze obtained from XRF. The relationship between the chemical compositions of glaze layers and the OCT image feature are discussed based on the results of SEM-EDS and LRS.
钟丹霞, 郭木森, 胡永庆, 刘松, 董俊卿, 李青会. 基于光学相干层析成像的古代瓷器釉层分类[J]. 中国激光, 2018, 45(1): 0104001. Zhong Danxia, Guo Musen, Hu Yongqing, Liu Song, Dong Junqing, Li Qinghui. Microstructures of Ancient Porcelains Based on Optical Coherence Tomography[J]. Chinese Journal of Lasers, 2018, 45(1): 0104001.