Iterative Optimal Threshold Segmentation

Iterative Optimal Threshold Segmentation - The algorithm begins by selecting the initial threshold as the average grayscale value T0 of the image. It then partitions the image pixels into two groups using T0, calculates the average grayscale values for each group (TA for pixels below T0, TB for pixels above T0), and computes a new threshold T1 as the average of TA and TB. This process iterates recursively, updating the threshold at each step until convergence is achieved when TK+1 equals TK. The method demonstrates how iterative computation can progressively refine the segmentation boundary for optimal image partitioning.