Automatic Co-Registration of Pan-Tilt-Zoom (PTZ) Video Images with 3D Wireframe Models
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Bibliographic record
Abstract
Abstract We propose an algorithm for the automatic co-registration of Pan-Tilt-Zoom ( ptz ) camera video images with 3 D wireframe models. The proposed method automatically retrieves changing camera focal length and angular parameters, due to the motion of ptz cameras by matching linear features between ptz video images and 3 d cad wireframe models. The developed feature-matching schema is based on a novel evidence-based hypothesis-verification optimization framework referred to as Line-based Randomized ran dom sa mple Consensus ( lr-ransac ). lr-ransac introduces a fast and stable pre-verification test into the optimization process to avoid unnecessary verification of erroneous hypotheses. An evidence-based verification follows to optimally select the ptz camera parameters, where an original line-based approach for full-verification, -exploiting local geometrical cues on the image scene-, evaluates the pre-verified hypotheses. Tests on an indoor dataset produced a 0.06 mm error in focal length estimation and rotational errors in the order of 0.18° to 0.24°. Experiments on the outdoor dataset resulted in a 0.07 mm error for focal length and rotational errors ranging from 0.19° to 0.30°.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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