Molada-Tebar Adolfo, Verhoeven Geert J, Hernández-López David, González-Aguilera Diego
Department of Cartographic and Land Engineering, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros 50, 05003 Avila, Spain.
Department of Prehistoric and Historical Archaeology, University of Vienna, Franz-Klein-Gasse 1, 1190 Vienna, Austria.
Sensors (Basel). 2024 Mar 7;24(6):1743. doi: 10.3390/s24061743.
Color data are often required for cultural heritage documentation. These data are typically acquired via standard digital cameras since they facilitate a quick and cost-effective way to extract RGB values from photos. However, cameras' absolute sensor responses are device-dependent and thus not colorimetric. One way to still achieve relatively accurate color data is via camera characterization, a procedure which computes a bespoke RGB-to-XYZ matrix to transform camera-dependent RGB values into the device-independent CIE XYZ color space. This article applies and assesses camera characterization techniques in heritage documentation, particularly graffiti photographed in the academic project INDIGO. To this end, this paper presents COOLPI (COlor Operations Library for Processing Images), a novel Python-based toolbox for colorimetric and spectral work, including white-point-preserving camera characterization from photos captured under diverse, real-world lighting conditions. The results highlight the colorimetric accuracy achievable through COOLPI's color-processing pipelines, affirming their suitability for heritage documentation.
文化遗产记录通常需要颜色数据。这些数据通常通过标准数码相机获取，因为它们提供了一种从照片中提取RGB值的快速且经济高效的方法。然而，相机的绝对传感器响应取决于设备，因此不是色度学的。一种仍然能够获得相对准确颜色数据的方法是通过相机表征，这是一个计算定制的RGB到XYZ矩阵的过程，用于将依赖于相机的RGB值转换为与设备无关的CIE XYZ颜色空间。本文应用并评估了相机表征技术在遗产记录中的应用，特别是在学术项目INDIGO中拍摄的涂鸦。为此，本文介绍了COOLPI（用于处理图像的颜色操作库），这是一个基于Python的新颖工具箱，用于色度学和光谱工作，包括在各种真实世界照明条件下拍摄的照片中进行白点保留相机表征。结果突出了通过COOLPI的颜色处理管道可实现的色度学准确性，证实了它们适用于遗产记录。
