RainbowTag: a Fiducial Marker System with a New Color Segmentation Algorithm
Why this work is in the frame
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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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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 | 0.000 | 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