Colour constancy based on texture similarity for natural images
Why this work is in the frame
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Bibliographic record
Abstract
Colour constancy is a classical problem in computer vision. Although there are a number of colour constancy algorithms based on different assumptions, none of them can be considered as universal. How to select or combine these available methods for different natural image characteristics is an important problem. Recent studies have shown that the texture feature is an important factor to consider when selecting the best colour constancy algorithm for a certain image. In this paper, Weibull parameterisation is used to identify the texture characteristics of colour images. According to the texture similarity, the best colour constancy method (or best combination of methods) is selected out for a specific image. The experiments were carried out on a large data set and the results show that this new approach outperforms current state‐of‐the‐art single algorithms, as well as some combined algorithms.
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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