Measurements of soiling and colour change using outdoor rephotography and image processing in Adobe Photoshop along the southern façade of the Ashmolean Museum, Oxford
Bibliographic record
Abstract
Abstract This paper builds on work using Adobe Photoshop as image-processing software to obtain (histogram-based) quantification of camera-captured images. The analysis tracks cross-temporal surface colour change (in 2005 and 2007) at the façade-scale of a limestone building (cleaned between 2006 and 2007) by means of digital photographs obtained in repeat photographic surveys taken under different outdoor lighting conditions (of a clear sky v. overcast). The relevance of the study is to contribute to further research using the integrated digital photography and image processing (IDIP) method in an outdoor setting (O-IDIP) with differing levels of light at a façade- (building) scale necessary for assessments of soiling affecting decisions of maintenance and restoration. Calibration was performed using spectrophotometric data (acquired in the winter of 2006). Findings show that the calibrated method is able to measure change before v. after the cleaning of the southern façade, which was darker in 2005 (with a lower level of lightness), especially at the east elevation. Lightness (surface darkening or blackening) is more affected by outdoor lighting conditions than chromatic values along green–red ( a ) and blue–yellow ( b ) channels of colour. These findings confirm that there is more error associated with soiling measurements at the façade-scale (in an outdoor setting).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".