Application of Spectral Estimation Techniques to the Improvement of a 3D-color Digitizing Camera
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it