Source Code Implementation of a Novel Edge Detection Technique Combining Cellular Learning Automata and Fuzzy Systems

This paper presents a novel edge detection technique that integrates cellular learning automata with fuzzy systems to achieve higher precision in identifying image edges. The cellular learning automata component implements a cell-based computational model that mimics neuronal learning behaviors in neural networks, where each cell autonomously adjusts its state through reinforcement learning mechanisms. The fuzzy system module employs mathematical tools for processing uncertain information, utilizing membership functions and fuzzy rule bases to effectively handle ambiguous pixel data in images. Key implementation aspects include: - CLA-based edge probability calculation using neighborhood state transitions - Fuzzy inference system for edge strength evaluation with triangular membership functions - Adaptive thresholding algorithm that dynamically adjusts based on local image characteristics - Matrix convolution operations for gradient computation with customizable kernel sizes By synergistically combining these two methodologies, we have developed an innovative edge detection approach that demonstrates significant advantages in digital image processing applications, particularly in handling noisy images and preserving fine edge details through its dual-layer intelligent processing architecture.