An Active Contour Model Based on Local Pre-Piecewise Fitting Bias Corrections for Fast and Accurate Segmentation
Why this work is in the frame
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Bibliographic record
Abstract
The lack of grasp of the image information and the unstable fluctuation of the model energy may cause segmentation failure of the active contour model (ACM). Minimizing the impact of these two factors is critical. A local pre-piecewise fitting (LPPF) bias correction (BC) model for fast and accurate segmentation is proposed in this article. It defines a prefitting function of local regions and an energy function. The grayscale information of small areas in the image is fully extracted, so that the contour accurately locates the target. Then, the optimal solution to the estimated value of the bias field is obtained. The real image information is described by the bias field, and the energy function of the model is constructed. The optimized distance regularized term and neighborhood average filtering method are utilized to achieve level set function regularization and contour smoothing. This optimization process reduces the amount of calculation and improves the robustness of LPPF model. Experiments are performed to verify that LPPF model has strong robustness to initial contours and has ability to segment blurry images while satisfactory segmentation efficiency and accuracy are obtained.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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