Modeling perceptual color confusion of helmet-mounted display symbology as a function of see-through contrast
Bibliographic record
Abstract
In military aviation helmet-mounted displays (HMDs) or head-up displays, light from the ambient scene is added to the symbology to create a complex mixture of colors, textures, and luminances. In the case of color mixing, the color of the transparent symbology symbols shifts toward the colors of the ambient background that the symbology overlays. The magnitude of the shift depends on the contrast of the symbology against the background. Against a darkened background, there is negligible shifting of symbology color. However, during daylight conditions, symbology colors shift toward the background hue. Using CIELAB distances between symbology colors as a measure of color discrimination, confusion contrast thresholds are calculated for each of seven symbology colors mixed with fourteen different background colors over a wide range of luminance contrasts. Confusion contrast thresholds are calculated for color normal and color vision deficient (CVD) observers. For CVD observers, colors are filtered using the RGB coefficients developed by Machado. Using the color discrimination data presented here as well as previous assessments of HMD luminance requirements based on observer ratings of the quality of symbology, luminance guidelines for see-through displays are presented, which correct for a calculation error made previously.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".