基于改进的二维最大熵及粒子群递推的图像分割
Thresholding Based on Improved 2D Maximum Entropy Method and Particle Swarm Optimization
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摘要: 鉴于常用的灰度级-平均灰度级二维直方图区域划分存在明显的不足,提出一种灰度级(平均灰度级)-梯度二维直方图及其区域划分方法.给出了改进二维直方图的最大熵阈值选取公式,并利用粒子群算法寻找最佳阈值,在迭代过程中采用递推算法,大大地减少了适应度函数的重复计算.实验结果表明,该方法不仅使分割后的图像区域内部均匀、边界形状准确、抵抗噪声稳健,同时相对粒子群算法运算速度又提高了约1倍.Abstract: 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.
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