Automatic Co-Registration of Pan-Tilt-Zoom (PTZ) Video Images with 3D Wireframe Models
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".