A Novel Edge Detection Algorithm Based on Outer Totalistic Cellular Automata
Bibliographic record
Abstract
Edge detection is a key technique in image processing. The detected edge quality has a direct and significant impact on performance. There is a multitude of methods for edge detection but they are strongly associated with the application and the quality of the images. However, more precise outcomes and a reduced execution time remain the primary objectives for extracting edges. To address these issues, we propose a novel technique based on a complex system called Cellular Automata (CA). They are successfully applied in edge detection due to their simplicity and local interactions. This undertook shed new light on a novel method using Outer Totalistic Cellular Automata (OTCA) to perform efficiently edge detection. We have tested images from Berkeley dataset. RMSE and SSIM are used as fitness functions for estimating numerical performance of OTCA rules. Comparisons were made with classical edge detectors like: Canny, Scharr, Sobel, Roberts. Experimental results showed that OTCA rules provide excellent performance and outperforms other edge detectors in terms of precision and execution time, particularly when dealing with noisy 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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 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".