OTSU Algorithm for Optimal Threshold Calculation: A Simplified Implementation

This content discusses MATLAB and the OTSU algorithm, which is a straightforward image processing technique. The OTSU algorithm calculates the optimal threshold for image binarization by dividing image gray values into two classes to minimize within-class variance while maximizing between-class variance. In MATLAB implementation, you can use built-in functions like graythresh() which employs the OTSU method, typically combined with imbinarize() for actual binarization. The algorithm works by iterating through all possible threshold values and computing the weighted variance between foreground and background pixels. Besides OTSU, numerous other image processing algorithms are available, such as Canny edge detection (using edge() function with 'canny' parameter) and Sobel operators (using edge() with 'sobel' option), each with distinct advantages and specific application domains. When selecting image processing algorithms, considerations should include computational efficiency, noise sensitivity, and the specific requirements of your application scenario.