Markov Random Field Example Implementation

This documentation presents a comprehensive Markov Random Field (MRF) example program that implements both Iterated Conditional Modes (ICM) and Belief Propagation (BP) algorithms. The MATLAB-based implementation comprises over 30 modular functions, each designed for specific computational tasks such as energy function calculation, message passing routines, neighborhood system initialization, and inference optimization. Key algorithmic components include: ICM's iterative local optimization approach for pixel-wise label updates, and BP's message passing mechanism for marginal probability computation across graph nodes. This program serves as a practical framework for solving real-world problems including image segmentation, speech recognition, and natural language processing tasks. By studying this implementation, researchers can gain deeper insights into MRF concepts and applications, enabling effective integration of these techniques into their own projects for improved computational outcomes and research efficiency.