Application of Spectral Estimation Techniques to the Improvement of a 3D-color Digitizing Camera
Bibliographic record
Abstract
This paper reports on the colorimetric improvement of a multispectral 3D digitizer through scanning at optimized wavelengths. These wavelengths were first established theoretically based on the criteria of minimal CIEDE2000 color difference over the set of reflectance curves from the full OSA-UCS catalog. A PCA-based and a spline-based spectral estimation method were considered, and sets of three, four and five optimal sampling wavelengths were derived for each method. This provided a basis for the selection of HeCd, ArKr, HeNe and DPSS commercial laser lines for which the colorimetric performance was predicted. This was then tested in the lab, where colour rendition charts were scanned with the camera at seven wavelengths, after which the charts were computer-rendered on a CRT display. Both the theoretical prediction and the experimental observation indicate that four well-chosen wavelengths are adequate for proper rendition of the charts.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".