Laplacian of Logarithm as Illuminant-Invariant Input Space
Why this work is in the frame
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Bibliographic record
Abstract
An object’s color is affected by the color of the light incident upon it, and the illuminant-dependent nature of color creates problems for convolutional neural networks performing tasks such as image classification and object recognition. Such networks would benefit from illuminant-invariant representation of the image colors. The Laplacian of the logarithm of the image is introduced as an effective color invariant. Applying the Laplacian in log space makes the input colors approximately illuminationinvariant. The illumination invariance derives from the fact that finite-difference differentiation in log space is equivalent to ratios of neighboring pixels in the original space. For narrow-band sensors, rationing neighboring pixels cancels out their shared illumination component. The resulting color representation is no longer absolute, but rather is a relative color representation. Testing shows that when using the Laplacian of the logarithm as input to a Convolutional Neural Network designed for classification its performance is: (i) approximately equal to that of the same network trained on sRGB data under white light, and (ii) largely unaffected by changes in the illumination.
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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) | 0.001 | 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