Gaussian Mixture Model Algorithm Implementation

This program implements Gaussian Mixture Model (GMM) algorithms using MATLAB for comprehensive image processing capabilities. The algorithm utilizes MATLAB's built-in functions like fitgmdist() for parameter estimation and gmcluster() for data clustering, enabling sophisticated image manipulation including image enhancement through histogram modeling, edge detection via probability distribution analysis, and image segmentation using expectation-maximization (EM) optimization. By employing Gaussian Mixture Models, the algorithm achieves more precise and efficient image processing through statistical modeling of pixel distributions, resulting in superior image quality and visual effects. The implementation allows customization through adjustable parameters such as the number of Gaussian components, covariance matrix types, and convergence thresholds, enabling optimization for various processing scenarios. Key functions include component initialization using k-means clustering, iterative parameter updates via maximum likelihood estimation, and posterior probability calculations for pixel classification. Overall, the MATLAB-based Gaussian Mixture Model algorithm serves as a powerful and flexible tool for diverse image processing tasks, providing researchers and engineers with robust statistical methods for computer vision applications.