Bibliographic record
Abstract
The subject of Detecting edges in images is considered one of the main topics in digital image processing and the most common one, due to its wide applications in many fields. Classical methods for detecting edges in digital images still give excellent results if the threshold is chosen correctly. In this paper, a group of classical edge algorithms was taken and tested on different types of images, Canny edge detection algorithm gave the best results in all circumstances if the threshold value of it was set between 0.30-0.45. The range of the threshold values was from 0.1 to 0.45 in Roberts, Sobel, and Prewitt's edge detection algorithms. In this paper, four famous classical algorithms were tested on some standard RG BA images which had some noises by purpose and by holding different frequencies, containing different types of noise. Since the number of images has reached 50 thousand in total, a lot of data has been obtained and these algorithms have been tested on a large number of images. Indicating the algorithms implemented to perform on different image types, the threshold value was changed from 0 to 1 thousand times with each image by 0.001 value.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".