ARTag Revision 1, A Fiducial Marker System Using Digital Techniques
Bibliographic record
Abstract
ARTag is a 2D marker and computer vision system for Augmented Reality, a Fiducial marker system, that was introduced in a prior NRC publication [6]. Augmented Reality (AR) is an emerging display paradigm, an enabling technology of AR is vision based pose tracking. Pose can be found accurately and with low cost using a camera as the only special hardware. Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital images using an accompanying detection algorithm. They are useful for AR, robot navigation, and general applications where the relative pose between a camera and object is required. ARTag is a marker system that uses digital coding theory to get a very low false positive and inter-marker confusion rate with a smaller required maker size, employing an edge linking method to give robust lighting and occlusion immunity. ARTag markers are bi-tonal planar patterns that consist of a square outline with a digital 36-bit word encoded in the interior. The digital word contains a unique ID number protected from false detection with the digital code techniques of checksums and forward error correction (FEC) providing very low and numerically quantifiable error rates. ARTag's performance is theoretically or experimentally examined for nice characteristics import to AR; false positive and false negative detection rates, inter-marker confusion probabilities, immunity to lighting, immunity to occlusion, minimal marker size, vertex jitter, marker library size, and speed performance. This publication further characterizes ARTag and provides more detailed information and experimental results useful for those interested in utilizing ARTag, and those interested in fiducial marker systems themselves.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.030 |
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".