Edge Detection Algorithm Using Cellular Automata for Image Processing

This is a MATLAB-implemented algorithm that utilizes cellular automata for image edge detection. The algorithm employs cellular automata rules to process pixel neighborhoods, where each cell's state transitions based on its surrounding pixels' values to identify edge boundaries. Key implementation aspects include: - Initialization of cellular states from grayscale image pixel intensities - Application of transition rules considering Moore or von Neumann neighborhoods - Iterative updating of cell states until edge patterns stabilize - Threshold-based edge classification and morphological operations for refinement By leveraging cellular automata techniques, the algorithm achieves precise edge detection through localized computations, enhancing image processing quality and accuracy. The implementation integrates various image processing techniques including noise reduction filters, gradient calculations, and binary image operations to meet diverse user requirements. This MATLAB-based edge detection method demonstrates significant potential in computer vision applications by providing an alternative approach to conventional edge detection operators like Sobel or Canny filters.