Spectral reflectance estimation from non‐raw color images with nonlinearity correction
Bibliographic record
Abstract
Abstract The spectral reflectance is recognized as the fingerprint of an object surface and has been used to achieve accurate color measurement in textile and other fields. Spectral reflectance can be recovered from color images to preserve high spectral and spatial resolutions simultaneously. However, a color camera commonly supplies a non‐raw color image, which is non‐linear with respect to the scene radiance, and is inappropriate for quantitative analysis. In this study, for non‐raw color images, different nonlinearity correction models are designed and evaluated with respect to different spectral estimation algorithms. The colorimetric and spectral accuracy of spectral estimation after the nonlinearity correction is assessed through both simulation and practical experiments. In the simulation, a large number of spectral images from several datasets are employed to directly verify the effectiveness of the nonlinearity correction. In the practical experiments, the spectral estimation accuracy following the nonlinearity correction is verified directly and indirectly based on actual color images. The resulting linear color image data after the nonlinearity correction can provide better spectral estimation accuracy especially for the PI algorithm with one power‐function based model. Besides, the combination of the simple PI algorithm with the power‐function based model can exceed other combinations comprising complex algorithms and models in both accuracy and efficiency. For the linear color image data, the PI algorithm even surpasses the deep learning‐based methods in certain metric, thus indicating a shallow relationship exists between the linear color image data and the spectral reflectance.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".