Bilateral filter based mixture model for image segmentation
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a bilateral filtering based mixture model for image segmentation. The mixture model uses Markov Random Field (MRF) to incorporate spatial relationship among neighboring pixels into the Gaussian Mixture Model (GMM) in order to perform a segmentation that is robust against noise and other environmental factors. The bilateral filtering is used to smooth the posterior probability map as part of the MRF used. The advantage of the proposed model is its simplified structure so that the Expectation Maximization algorithm can be directly applied to the log-likelihood function to compute the optimum parameters of the mixture model. The method has been extensively tested on synthetic and natural images and compared with some of the state-of-the-arts algorithms currently available. The experimental results show that the proposed method is comparable to the other methods in terms of accuracy and quality and simpler in terms of implementation.
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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 | 0.000 | 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 | 0.000 |
| 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