This paper will present the first module of an advanced set of metadata and knowledge management tools to record a "Digital Lab Notebook" (DLN), the equivalent of the traditional scientist's lab notebook. The DLN:Capture Context (DLN:CC) tool describes the means and context of photographic data capture. The tool is designed for broad use across computational photography technologies. The DLN:CC has already been implemented for Reflectance Transformation Imaging (RTI) and implementation for photogrammetry is underway. The collection and organization of contextual metadata is highly automated, facilitating use during the time the image data is captured and processed, rather than afterward. This project adds ISO-standard compliant metadata, which establishes the provenance of the imaging subject's digital surrogate. The captured photographic sequences and the DLN metadata contain all the information needed to generate and/or regenerate advanced, image-based 2D and 3D digital surrogates, such as Reflectance Transformation Imaging or photogrammetry's 3D models with texture. The DLN also provides each digital surrogate a scientific account of their collection and generation.
Carla Schroer, Mark Mudge, "A Context Metadata Collection and Management Tool for Computational Photography Projects" in Proc. IS&T Archiving 2017, 2017, pp 99 - 104, https://doi.org/10.2352/issn.2168-3204.2017.1.0.99