Deteksi Tepi Optimal dengan Integrasi Canny, CLAHE, dan Perona-Malik Diffusion Filter
Bibliographic record
Abstract
Edge detection is a fundamental technique in digital image processing, crucial for identifying object boundaries. However, detecting edges in low-intensity and noisy images remains a significant challenge. This study proposes an optimal edge detection method by integrating the Canny algorithm, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Perona-Malik Diffusion Filter, with automatic kappa (k) value determination using the Fractional Order Sobel Mask. The process begins with noise reduction through the Perona-Malik Diffusion Filter, followed by local contrast enhancement using CLAHE, and concludes with edge detection via the Canny algorithm. Experimental results demonstrate that the proposed method significantly enhances edge clarity and robustness against noise compared to the conventional Canny algorithm, particularly for low-intensity images and images with noise. Tests on leaf and medical images confirm the effectiveness of this method in improving edge detection quality in digital images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".