5 - Propagation de l'incertitude dimensionnelle dans le problème de l'ajustement d'ellipses. Application à la reconnaissance automatique de formes elliptiques dans les images
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
The conic fitting from image points is a very old topic in estimation and pattern recognition. This problem gave rise to a lot of studies and arouses interests still today. Systematically, these works have been based on the algebraic representation of the conic to establish the optimization criteria. Less studied, the polar representation of the ellipse is costlier because it needs the optimization of the parametrization. Yet, we propose in this paper some new ideas about this question. First, we show that the estimation of the parameters and the parametrization separated permit to make the problem easier leading to a direct inversion and the search of the roots of a four degree polynomial respectively. We also show that the parametrization carries the dimensional characteristics of the ellipse and when it is correctly disrupted in the minimization process, we constraint the ellipse search space. This new result gives an estimate without dimensional bias in a noised and incomplete context. A confidence envelope is then estimated to direct the search for continuations of the ellipse. At last, we propose a hierarchical grouping and fitting stage following with a fuzzy decision step to detect automatically the elliptic shapes in the 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 | 0.002 | 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 | 0.001 | 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