A Foveon Sensor/Green-Pass Filter Technique for Direct Exposure of Traditional False Color Images
Bibliographic record
Abstract
A direct exposure technique to simulate the vegetation red-magenta hues associated with the traditional Kodak Ektachrome Professional Infrared EIR film is proposed. The method uses the Foveon sensor in full spectrum mode as found in Sigma cameras in combination with green-pass filters to reproduce the red-magenta hues of healthy vegetation without resorting to multi-exposures or channel swaps as is the current norm in single sensor cameras. An array of commercially available green-pass filters are analyzed via spectrophotmetric, densitometric and colorimetric measurements. A calibration technique using color-compensating filters is presented such that any green-pass filter will transmit green wavelengths to a set norm and ensure repeatability. A color model for the Foveon/green-pass filter is developed using quantum efficiencies to explain the color effects observed. Color similarities in near-infrared, blue and black and differences in red and green compared with Kodak Ektachrome Infrared film are discussed. Aerial photography examples are discussed, and the technique is proposed as a more direct means of obtaining traditional false color infrared imagery for pictorial and scientific applications when using Foveon type commercially available digital cameras.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".