Overlay Videos for Quick and Accurate Watermark Identification, Comparison, and Matching: Creating and Using Overlay Videos
Bibliographic record
Abstract
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in this case Jan Gillisz van Vliet (1600/10–1668); and 3) between selected Rembrandt prints and contemporaneous Dutch historical documents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".