Open AccessA Novel Edge Detection Algorithm Based on Outer Totalistic Cellular Automata
Author(s) -
Safia Djemame,
Siham Fichouche
Publication year - 2022
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.360103
Subject(s) - sobel operator , enhanced data rates for gsm evolution , cellular automaton , edge detection , computer science , canny edge detector , image (mathematics) , algorithm , image gradient , detector , key (lock) , image quality , artificial intelligence , image processing , pattern recognition (psychology) , telecommunications , computer security
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.