Enhanced Canny Algorithm for Image Edge Detection in Print Quality Assessment
Bibliographic record
Abstract
The growing demand for high-quality print output in the digital printing era underscores the importance of refining detection algorithms essential for print quality assessment systems.This study focuses on the analysis and optimization of the classical image edge detection algorithm, the Canny algorithm.A novel method is presented, which incorporates an improved adaptive median filter (AMF) for the initial processing of images, resulting in increased efficiency and better handling of noise points.Furthermore, the gradient calculation direction has been expanded, and the threshold has been fine-tuned using an enhanced OTSU algorithm.The optimal threshold selection relies on a preliminary judgement, leading to more comprehensive and accurate image edge information capture.Comparative analysis with the Sobel operator and the traditional Canny edge detection highlights the advantages of the optimized Canny algorithm.This improved approach succeeds in preserving a greater amount of graphical edge information and exhibits a superior ability to identify false edges, significantly increasing detection accuracy.The findings of this study contribute to the development of print quality detection, promoting a more automated, digital, and systematic approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".