Principle of Maximum Inter-Class Variance Method (Otsu's Method)

The Principle of Maximum Inter-Class Variance Method: Otsu's Method is an adaptive thresholding algorithm for image segmentation proposed by Japanese scholar Otsu in 1979. The core concept involves determining the optimal threshold by maximizing inter-class variance on the image histogram. Inter-class variance refers to the variance obtained when dividing the image into two classes (foreground and background) at a given threshold - when this variance reaches its maximum, the threshold is considered optimal for achieving the best segmentation results. In code implementation, the algorithm typically iterates through all possible threshold values (0-255 for 8-bit images), calculates between-class variance using probability distributions, and selects the threshold yielding maximum separation. This method has gained widespread application in image processing and remains one of the most classical image segmentation algorithms due to its computational efficiency and effectiveness in bimodal histograms.