Cellular Automata Example Program in MATLAB Environment

This provides a cellular automata example program developed within the MATLAB environment. Cellular automata represent a mathematical modeling framework for complex systems, utilizing fundamental concepts such as cells, states, and transition rules to simulate dynamic system evolution. In this demonstration program, we showcase the application of cellular automata in image processing tasks. The implementation features several key functions including: - Image segmentation through state-based cell classification - Spatial filtering using neighborhood rule applications - Noise reduction via iterative state updating algorithms The program is structured around MATLAB's matrix operations, where each pixel is treated as an individual cell with discrete states. The core algorithm implements Moore neighborhood (8-connected) or von Neumann neighborhood (4-connected) configurations for state transitions. Key functions include: 1. `ca_initialize()` - Sets up initial cell states from input images 2. `apply_rules()` - Implements transition rules for cell state updates 3. `visualize_states()` - Displays intermediate and final processing results For researchers interested in cellular automata theory and practical image processing applications, we recommend examining the implementation details of the state transition logic and neighborhood configurations. The modular code structure allows for straightforward modifications and extensions to explore additional applications such as pattern recognition, texture synthesis, and computational biology simulations.