Removing Outliers in Illumination Estimation
Why this work is in the frame
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Bibliographic record
Abstract
A method of outlier detection is proposed as a way of improving illumination-estimation performance in general, and for scenes with multiple sources of illumination in particular. Based on random sample consensus (RANSAC), the proposed method (i) makes estimates of the illumination chromaticity from multiple, randomly sampled sub-images of the input image; (ii) fits a model to the estimates; (iii) makes further estimates, which are classified as useful or not on the basis of the initial model; (iv) and produces a final estimate based on the ones classified as being useful. Tests on the Gehler colorchecker set of 568 images demonstrate that the proposed method works well, improves upon the performance of the base algorithm it uses for obtaining the sub-image estimates, and can roughly identify the image areas corresponding to different scene illuminants.
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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