Designing Highly Reliable Fiducial Markers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fiducial markers are artificial landmarks added to a scene to facilitate locating point correspondences between images, or between images and a known model. Reliable fiducials solve the interest point detection and matching problems when adding markers is convenient. The proper design of fiducials and the associated computer vision algorithms to detect them can enable accurate pose detection for applications ranging from augmented reality, input devices for HCI, to robot navigation. Marker systems typically have two stages, hypothesis generation from unique image features and verification/identification. A set of criteria for high robustness and practical use are identified and then optimized to produce the ARTag fiducial marker system. An edge-based method robust to lighting and partial occlusion is used for the hypothesis stage, and a reliable digital coding system is used for the identification and verification stage. Using these design criteria large gains in performance are achieved by ARTag over conventional ad hoc designs.
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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 | 0.001 |
| 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