RainbowTag: a Fiducial Marker System with a New Color Segmentation Algorithm
Bibliographic record
Abstract
We introduce a new color-based fiducial marker system-RainbowTag (RT)-for detection and identification that is suitable for autonomous navigation due to robustness to varying lighting conditions, motion blur, partial occlusion and folding. This system uses cameras already present on the vehicles to complement spatial information estimated from other sensors (e.g., Global Positioning System, inertial measurement, radar). RT is composed of a fiducial marker design and its adapted detection algorithm. Numerous real-world experiments demonstrate that markers can be reliably detected in various lighting conditions, in the presence of large motion blur, and even when folded or partially occluded. In all test conditions, RT outperforms the fiducial markers Aruco and ChromaTag. Compared to other blur-resistant fiducials that are circularly symmetric [1], [2], RT has the advantage that it encodes orientation information. Our detection algorithm is powered by a novel color segmentation approach that carefully orchestrates information from the hue constant IPT, the perceptually uniform CIELAB, and the Bradford LMS cone response color spaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".