Implementation of Otsu's Thresholding Algorithm for Image Segmentation

The implementation of Otsu's thresholding algorithm discussed in this article is accomplished through low-level programming rather than simply calling pre-built functions. This algorithm is widely used in image processing to automatically distinguish between foreground and background regions in images. Otsu's method operates by analyzing the image's grayscale histogram to find an optimal threshold that separates the image into two distinct classes: foreground and background. The threshold is determined by calculating the between-class variance across all possible threshold values and selecting the one that maximizes this variance. This approach ensures optimal separation between foreground and background based on the image's grayscale distribution. From an implementation perspective, this involves iterating through all possible threshold values (typically 0-255 for 8-bit images), computing the weight and mean for both classes at each threshold, and calculating the between-class variance using the formula: σ² = ω₁ω₂(μ₁-μ₂)². The algorithm effectively segments images into clear foreground and background regions, providing a fundamental basis for subsequent image processing tasks such as object detection and image analysis.