Iterative Markovian estimation of mass functions in Dempster Shafer evidence theory: application to multisensor image segmentation
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Bibliographic record
Abstract
Mass functions estimation is a key issue in evidence theory-based segmentation of multisensor images. In this paper, we generalize the statistical mixture modeling and the Bayesian inference approach in order to quantify the confidence level in the context of Dempster-Shafer theory. We demonstrate that our model assigns confidence levels in a relevant manner. Contextual information is integrated using a Markovian field that is adapted to handle compound hypotheses. The multiple sensors are assumed to be corrupted by different noise models. In this case, we show the interest of using a flexible Dirichlet distribution to model the data. The effectiveness of our method is demonstrated on synthetic and radar and SPOT 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 | 0.000 |
| 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