Identifying, comparing, and matching watermarks in pre-machine-made papers has occupied scholars of prints and drawings for some time. One popular but arduous approach is to overlay, either manually or digitally, an image of the watermark in question with its presumed match from a known source. For example, a newly discovered watermark in a Rembrandt print might be compared to a similar one reproduced in Erik Hinterding’s Rembrandt as an Etcher (2006). Such an overlay can confirm the pair as identical, i.e., as moldmates, or reveal their differences. But creating an accurate overlay for two images with different scales, orientations, or resolutions using standard image-manipulation tools can be time consuming and, ultimately, unsuccessful. Part One of this article describes advances in the emerging field of computational art history, specifically the development of digital image processing software, that can be used to semi-automatically create a reliable animated overlay of two watermarks, regardless of their relative “comparability.” Watermarks found in the prints of Rembrandt van Rijn (1606–1669) are used in three case studies to demonstrate the efficacy of user-generated overlay videos. Part Two discusses how searching for identical watermarks, i.e., moldmates, can be enhanced through the application of a new suite of software programs that exploit the data calculated during the creation of user-generated animated overlays. This novel watermark identification procedure allows for rapid, confident watermark searches with minimal user effort, given the existence of a pre-marked library of watermarks. Using a pre-marked library of Foolscap with Five-Pointed Collar watermarks, four case studies present different categories of previously undocumented matches 1) among Rembrandt’s prints; 2) between prints by Rembrandt and another artist, in this case Jan Gillisz van Vliet (1600/10–1668); and 3) between selected Rembrandt prints and contemporaneous Dutch historical documents.
Current status and prospects for quantitative analysis of digital image of pathological specimen using image processing software including artificial intelligence
