Edge Segmentation Algorithm Based on Morphological Gradient Vector
Why this work is in the frame
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Bibliographic record
Abstract
The image edge is explained by the gradient.As a vector variable,the gradient has two parts: the magnitude and the direction.The morphological gradient operator,i.e.a popular edge detection operator can detect only the magnitude of the image edge and cannot detect the direction of the image edge,thus lost the information of the edge gradient.This paper presents a new gray level morphological gradient method.The method points out that there is a morphological gradient operator with the direction estimate on the edge detection.The algorithm is validated theoretically and experimentally.The fuzzy process is added into the serial operators,so the noise in the image can be controlled and the clarity of the image edge be increased.Meanwhile,the optimal threshold segmentation is improved by adjusting the optimal threshold values of different directions.
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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) | 0.001 | 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