Thresholding Based on Improved 2D Maximum Entropy Method and Particle Swarm Optimization

In view of the obvious shortage of commonly used regional division of gray level-average gray level two-dimensional histogram,an improved two-dimensional histogram based on gray level(average gray level)-gradient and its corresponding regional division method are proposed.The formulas for threshold selection of maximum entropy based on the improved two-dimensional histogram regional division are derived.The particle swarm algorithm is used to search the best threshold.A recursion method is used in iteration to greatly reduce the repeat computations of fitness function.Experimental results show that the proposed algorithm not only achieves a good segmentation quality of uniform regions,accurate borders and robust noise resistances,but also the computation efficiency is promoted twofold compared with the particle swarm algorithm.