Object-color-signal prediction using wraparound Gaussian metamers
Why this work is in the frame
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Bibliographic record
Abstract
Alexander Logvinenko introduced an object-color atlas based on idealized reflectances called rectangular metamers in 2009. For a given color signal, the atlas specifies a unique reflectance that is metameric to it under the given illuminant. The atlas is complete and illuminant invariant, but not possible to implement in practice. He later introduced a parametric representation of the object-color atlas based on smoother "wraparound Gaussian" functions. In this paper, these wraparound Gaussians are used in predicting illuminant-induced color signal changes. The method proposed in this paper is based on computationally "relighting" that reflectance to determine what its color signal would be under any other illuminant. Since that reflectance is in the metamer set the prediction is also physically realizable, which cannot be guaranteed for predictions obtained via von Kries scaling. Testing on Munsell spectra and a multispectral image shows that the proposed method outperforms the predictions of both those based on von Kries scaling and those based on the Bradford transform.
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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