Fiducial marker indoor localization with Artificial Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
A vision based positioning system could be categorized into two groups. One analyzes an environment's scenery by matching the inputs with imaginary database to find the optimum result. The other uses fiduciary markers. In proposed method, the system uses fiduciary markers with a capital alphabet in it. When the known size fiduciary marker is captured by a camera, by using homography transformation, the 6-DOF camera pose with respect to the marker's local coordinate can be calculated. To recognize the character in the marker, Artificial Neural Network (ANN) with back-propagation training method is used. 12 unique features of a character are defined and used as inputs of ANN. Since more than 95% recognition rate is achieved in testing phase, the Optical Character Recognition (OCR) with ANN could be used as a marker detection method. The localization experimental result with the fiduciary marker shows that the proposed method could be a solution for indoor localization.
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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