An analytical method for the 3D-location estimation of circular features for an active-vision system
Why this work is in the frame
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Bibliographic record
Abstract
A closed-form analytical solution to the circular-feature problem, which arose in the context of a 3-D object recognition system, is presented. Compared to previous methods, it is mathematically simpler, provides a solution for the case in which there does not exist a priori knowledge concerning the radius of a marker, and can be extended and applied to general quadratic surfaces. In addition, the method is clearer from a geometrical viewpoint. The method was applied to a set of circles, located on a calibration plate, whose locations were known with respect to a reference frame. The camera was calibrated prior to the application of the method. Since various distortion factors had to be compensated in order to obtain accurate estimates of the parameters of the imaged circle, an ellipse, with respect to the camera's image frame, a sequential compensation procedure was applied to the input grey-level image. Experimental results showing the validity of the method are reported.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 0.000 |
| 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