Contours actifs avec a priori de forme basé sur la transformée de Fourier-Mellin analytique
Bibliographic record
Abstract
Dans cet article, nous proposons une méthode originale pour incorporer un a priori de forme dans un modèle de contours actifs basé région afin d’améliorer sa robustesse aux similitudes, bruit et occultations. Nous définissons un a priori de forme à partir du recalage des fonctions level set associées au contour actif et une forme de référence. Le recalage que nous proposons se base sur la corrélation de phase par la transformée de Fourier-Mellin analytique (TFMA). Cette représentation, dédiée aux images à niveaux de gris, permet de gérer simultanément plusieurs objets. Nous illustrons expérimentalement les capacités de ce nouvel a priori de forme à contraindre l’évolution du contour actif vers une forme cible. Enfin, nous mettons en évidence, sur des images de synthèse et réelles, son apport pour la segmentation d’images en présence de similitudes, d’occultations et de bruits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".