Linear anchored Gaussian mixture model for location and width computations of objects in thick line shape
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".