Comparing ARTag and ARToolkit plus fiducial marker systems
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 marker systems are systems of unique patterns and computer vision algorithms that help solve the correspondence problem, automatically finding features in different camera images that belong to the same object point in the world. Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital images using an accompanying detection algorithm, useful for augmented reality (AR), robot navigation, 3D modeling, and other applications. This work compares the two recently developed systems ARTag and ARToolkit Plus on their reliability, detection rates, and immunity to lighting and occlusion. Processing in fiducial systems are defined as two stages, unique feature detection and verification/identification. The systems are compared considering these stages, experimental results are shown.
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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