Multispectral image invariant to illumination colour, strength, and shading
Why this work is in the frame
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Bibliographic record
Abstract
We present here a method that makes use of multispectral image data and generates a novel "photometric-invariant multispectral image" for this type of data. For RGB, an invariant image has been constructed independent of the colour and intensity of the illuminant and of shading. To generate this image either a set of calibration images is required, or entropy information taken from a single image can be used to develop the parameters necessary to produce the invariant. Nonetheless, generating an invariant image remains a complex and error-prone task for RGB image data. For multispectral images, we show that photometric-invariant image formation is in essence greatly simplified. One of the requirements for forming an invariant is the necessity of narrowband-sensor sensors. Here this is the case, and we show that with the simple knowledge of peak sensor wavelengths we can generate a high-D multispectral invariant. The PSNR is shown to be high between the respective invariant multispectral features for multispectral images taken under different illumination conditions, showing lighting invariance for a per-pixel measure; and the s-CIELAB error measure shows that the colour error between the 3-D colour images used to visualize the output invariant high-D data is also small.
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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 | 0.001 | 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