Gaussian Model-Based Background Subtraction in MATLAB

In MATLAB, Gaussian model-based background subtraction represents a fundamental image processing technique designed to enhance image quality and readability. The Gaussian model serves as a probability distribution framework effectively employed for handling noise and interference signals in digital images. During implementation, the process typically begins with image preprocessing steps such as noise reduction and image smoothing using functions like imgaussfilt() or medfilt2(). Subsequently, the Gaussian mixture model (GMM) can be fitted to estimate background components through algorithms like fitgmdist() or custom implementations using mvnpdf() for multivariate normal distribution calculations. The estimated background is then subtracted from the original image using matrix operations (e.g., processed_image = original_image - background_model). Finally, supplementary techniques including image segmentation with watershed() or edge detection using edge() functions may be applied for advanced image analysis and improved interpretability of the processed results.