Subpixel location of discrete target images in close-range camera calibration: a novel approach
Why this work is in the frame
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Bibliographic record
Abstract
A novel approach is presented for the location of discrete targets images with subpixel accuracy. It can be adopted as first stage of any method that uses planar and no-planar grids with circular, square or cross shaped targets in order to calibrate close range cameras. The approach has been performed on several calibration sessions each of them followed by the three-dimensional reconstruction of a reference object that was previously measured through a coordinate measuring machine. The performance of the proposed approach has been expressed in terms of deviation between the reference coordinates and the corresponding ones provided by triangulation. The results obtained have also been compared with those achieved through ShapeCapture<sup>TM</sup>, a software widely used in photogrammetry for camera calibration and 3D model reconstruction from images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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