Cellular Automata Image Edge Detection with Algorithm Implementation

This paper presents a custom implementation of cellular automata-based image edge detection developed according to the principles outlined in "Cellular Automata in Image Processing." The implementation utilizes a state transition mechanism where each cell's state evolves based on its neighbors' states through iterative computation cycles. Key algorithm components include neighborhood configuration analysis (typically 8-connected or 4-connected grids), state update rules based on gradient calculations, and threshold-based edge classification. The code structure implements parallel processing concepts through matrix operations, where pixel intensity values undergo multiple generations of cellular automata evolution to enhance edge detection accuracy. Through this paper, readers can explore the potential of cellular automata in image processing applications and utilize the provided algorithm for practical edge detection tasks. The implementation demonstrates how cellular automata rules can be programmed using matrix convolution techniques and iterative state updates, providing insights into both theoretical foundations and practical coding approaches for image analysis. This work aims to facilitate better understanding and application of cellular automata techniques in digital image processing.