Calibrating an active omnidirectional vision system
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
This paper describes a straightforward process for calibrating an active vision system containing both pinhole perspective and omnidirectional cameras. The perspective cameras can be easily calibrated using standard methods. Unfortunately, these methods are not suitable for omnidirectional cameras. Methods that rely on iterative least squares optimization, using a set of known image-world correspondences, are adopted for omnidirectional cameras. To ensure unbiased estimation of camera parameters, an omnidirectional calibration rig is employed so that nearly the entire field of view contains known calibration points. Measurement uncertainties collected from each stage of calibration are then combined to estimate the overall system uncertainty. This calibration process is evaluated experimentally by estimating the location of known points using triangulation, where the results achieved are comparable with the estimated system uncertainties.
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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 | 0.002 |
| 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