03 - Segmentation d'images couleur par partitions de Voronoï
Bibliographic record
Abstract
Nous etudions le probleme de la segmentation de bas niveau pour les images couleur. L'approche proposee consiste a modeliser la segmentation d'une image comme une partition de Voronoi generalisee de son domaine. Dans ce contexte, segmenter une image couleur revient a definir une distance appropriee entre points de l'image et a choisir un ensemble de sites. La distance est definie en considerant les attributs de bas niveau de l'image et, en particulier, l'information fournie par la couleur. La demarche adoptee repose sur la division du probleme de la segmentation en trois sous-tâches successives, traitees dans le cadre des partitions de Voronoi: la pre-segmentation, la representation hierarchique et l'extraction de contours.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".