Classical Otsu Algorithm Implementation

The classical Otsu algorithm is a threshold-based binarization method widely used in image processing. The MATLAB implementation follows essential computational steps including image grayscale conversion, calculation of inter-class variance maximum, and optimal threshold determination for binarization. Key implementation aspects involve: - Grayscale conversion using rgb2gray() function - Histogram calculation for probability distribution - Iterative computation of between-class variance using vectorized operations - Optimal threshold selection through argmax function - Final binarization with imbinarize() or custom thresholding Mastering this algorithm requires fundamental image processing knowledge, but provides significant benefits for understanding practical digital image processing applications. The implementation demonstrates core concepts in automatic threshold selection and has broad applications in computer vision and pattern recognition systems.