Image Segmentation Using the EM Algorithm

<p>I would like to share an image segmentation implementation that utilizes the Expectation-Maximization (EM) algorithm. This code demonstrates a probabilistic approach to clustering pixels based on Gaussian Mixture Models (GMM), where the EM algorithm iteratively estimates parameters by alternating between expectation (E-step) and maximization (M-step) phases. The implementation handles pixel intensity values as input features and automatically determines cluster assignments through maximum likelihood estimation.</p> <p>The code is intentionally kept concise and well-commented, making it accessible for beginners to study fundamental computer vision techniques. It includes key functions for initializing GMM parameters, calculating posterior probabilities, updating mean/covariance matrices, and visualizing segmentation results. Before using this code, ensure you have installed required dependencies like NumPy and OpenCV, and familiarize yourself with the theoretical foundation of EM algorithm for understanding the convergence behavior and parameter estimation process.</p> <p>If you encounter any issues or require further clarification about the implementation details—such as the handling of multidimensional data, convergence criteria, or cluster validation methods—please feel free to ask for assistance.</p>