Research on image edge detection algorithms based on fractional-order differentiation
Why this work is in the frame
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Bibliographic record
Abstract
The image edge is a region in the image that exhibits a clear discontinuity and change, which can serve as a reflection of the most fundamental attributes of the image. The research concentration in the field of image processing and computer vision is also on edge detection technology, which is employed to ascertain the contour details between various objects and regions of the image. This paper first examines the principles and methodologies of traditional edge detection algorithms, succinctly outlining the advantages and disadvantages of the Roberts, Prewitt, Sobel, and Canny operators. It then introduces representative algorithms for image edge detection based on fractional-order differentiation. Finally, it compares traditional edge detection algorithms with those based on fractional-order differentiation through experimental analysis, demonstrating that the latter exhibits superior performance in contour continuity and detail integrity. The application of fractional-order differential theory in image processing demonstrates significant potential.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it