A Standardized Workflow for Illumination-Invariant Image Extraction
Bibliographic record
Abstract
The illumination-invariant image is a useful intrinsic feature latent in colour image data. The idea in forming an illumination invariant is to postprocess input image data by forming a logarithm of a set of chromaticity coordinates, and then project the resulting 2-dimensional data in a direction orthogonal to a special direction, characteristic of each camera, that best describes the effect of lighting change. Lighting change is approximately simply a straight line in the log-chromaticity domain; thus, forming a greyscale projection orthogonal to this line generates an image which is approximately independent of the illuminant, at every pixel. One application has been to effectively remove shadows from images. But a problem, addressed here, is that the direction in which to project is camera-dependent. Moreover, preprocessing with a spectral sharpening transform to linearly transform the sensor curves to more narrowband ones greatly improves shadow attenuation, but sharpening is also camera-dependent and we may not have information on the camera. So here we take a simpler approach and assume that every input image consists of data in the standardized sRGB colour space. Previously, this assumption has led to the suggestion that the built-in mapping of sRGB to XYZ tristimulus values could be used by going on to sharpen the resulting XYZ and then seeking for an invariant. Instead, here we sharpen the sRGB directly and show that performance is substantially improved this way. This approach leads to a standardized sharpening matrix for any input image and a fixed projection angle as well. Results are shown to be satisfactory, without any knowledge of camera characteristics.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.044 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".