A WORKFLOW FOR GEOMETRIC COLOUR PHOTOGRAPHY OF PAINTED SURFACES
Bibliographic record
Abstract
Abstract. Colour fidelity is vital when documenting painted surfaces. The 2.5D nature of many painted surfaces makes orthophotos and digital surface models (DSMs) common products of the documentation process. This paper presents a workflow to combine photographic and photogrammetric methods to produce aligned colour and depth (orthophotos and DSMs). First, two photogrammetric software (Agisoft Photoscan and Capturing Reality Reality Capture) were tested to determine if they adjusted the colour data during the processing stages. It was found that Photoscan can produce 16-bit orthophotos without manipulating the data; however, Reality Capture is currently limited to 8-bit results. When capturing a surface using photogrammetry, it is common to use the same data for colour and depth. The presented workflow, however, argues that better colour accuracy can be achieved by capturing the two datasets separately and combining them in photogrammetric software. The workflow is demonstrated through the documentation of an unnamed religious painting from the 17th century.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".