Otsu Algorithm - Maximum Between-Class Variance Method for Image Thresholding

In digital image processing, Otsu's algorithm (also known as the maximum between-class variance method or inter-class variance maximization method) can be employed for image segmentation and related applications. The MATLAB implementation of this algorithm enables automatic threshold calculation and image segmentation, enhancing precision and efficiency in image processing tasks. Otsu's algorithm works by iterating through all possible threshold values to maximize the variance between foreground and background classes, effectively separating an image into distinct regions. Key functions typically involve grayscale histogram calculation, probability distribution analysis, and between-class variance computation. This segmentation approach facilitates subsequent image processing operations such as object detection, edge extraction, and region analysis. Consequently, Otsu's algorithm holds significant potential for widespread application in computer vision and image analysis domains.