Subsurface imaging for panel paintings inspection: A comparative study of the ultraviolet, the visible, the infrared and the terahertz spectra
Bibliographic record
Abstract
Abstract Infrared (IR) reflectography has been used for many years for the detection of underdrawings on panel paintings. Advances in the fields of IR sensors and optics have impelled the wide spread use of IR reflectography by several recognized Art Museums and specialized laboratories around the World. The transparency or opacity of a painting is the result of a complex combination of the optical properties of the painting pigments and the underdrawing material, as well as the type of illumination source and the sensor characteristics. For this reason, recent researches have been directed towards the study of multispectral approaches that could provide simultaneous and complementary information of an artwork. The present work relies on non−simultaneous multispectral inspection using a set of detectors covering from the ultraviolet to the terahertz spectra. It is observed that underdrawings contrast increases with wavelength up to 1700 nm and, then, gradually decreases. In addition, it is shown that IR thermography, i.e., temperature maps or thermograms, could be used simultaneously as an alternative technique for the detection of underdrawings besides the detection of subsurface defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".