机器学习的光谱基准作为非传统的意大利绘画主义摄影的歧视性分析
Claudia Scatigno1, Lorenzo Teodonio1, Eugenia Di Rocco2
1CREF-Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Via Panisperna 89a c/o P.za del Viminale 1, 00184 Roma, Italy.
Polymers
|July 13, 2024
概括
这项研究使用X射线光和红外光谱学与机器学习来分析非传统的意大利摄影打印. 该方法确定了独特的元素和分子指纹,揭示了艺术家的技术和材料使用.
科学领域:
- 艺术保护科学 艺术保护科学
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
背景情况:
- 意大利画家主义运动 (1930年代前) 采用独特的手工制作的摄影技术.
- 艺术家们采用了秘密方法来实现超越标准流程的独特视觉效果.
- 分析这些非传统方法需要先进的分析技术.
研究的目的:
- 开发一种分析非传统摄影印刷品的方法.
- 揭示Piero Vanni收藏 (1889-1939) 中的艺术技术和材料使用.
- 提取用于识别元素和分子指纹的光谱基准.
主要方法:
- 结合的X射线光 (XRF) 和红外分析光谱学 (IRS).
- 应用无监督机器学习技术进行数据分析.
- 分析了一系列非传统的意大利摄影印刷品.
主要成果:
- 在银化物印刷中突出隐藏的元素 (例如,).
- 突出了相同的印刷技术中的微妙差异.
- 识别的颜料 (酸盐,氧化铁) 和材料的再利用.
- 建立了四个主要的光谱组,代表了艺术家的调色板 (二色盐,银化物,碳印,油印).
结论:
- 开发的方法学成功地揭示了隐藏的艺术技巧和材料组成.
- 光谱基准提供了不同摄影过程的元素和分子指纹.
- 该研究重建了非传统摄影的"艺术调色板",揭示了风格实践和材料再利用.
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