Linear anchored Gaussian mixture model for location and width computations of objects in thick line shape
Why this work is in the frame
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Bibliographic record
Abstract
To detect linear structure, model-based approaches using Hough and Radon transforms are often used but, are not recommended for thick line detection, whereas methods based on image derivatives need further tedious step-by-step processing. In this paper, a novel detection paradigm is presented, where the 3D image gray level representation is considered as finite mixture model of statistical distributions, called linear anchored Gaussian and parametrized by radius, angle and scale parameters dealing with structure location and thickness. These parameters could estimated by Expectation-Maximization algorithm. To rid the data of irrelevant information brought by nonuniform and noisy background, a modified EM algorithm is detailed. The proposed method gives very accurate results on real-world and synthetic images, where, for the latter with strong Gaussian blur and Additive White Gaussian Noise (σn=150), the mean estimation errors on the orientation, the distance from the origin and the thickness reach 0.35∘, 0.4 and 0.48 pixel, respectively.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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