基于改进的二维指数熵及混沌粒子群的阈值分割

鉴于现常用的灰度级—平均灰度级二维直方图区域划分存在明显的不足,提出了基于灰度级—梯度二维直方图的指数熵阈值选取方法,给出了基于改进的二维直方图的指数熵阈值选取公式,并利用混沌粒子群优化算法寻找最佳分割阈值,采用递推方式降低迭代过程中适应度函数的计算代价。实验结果表明,与现有的有关算法相比,该方法不仅使分割后的图像区域内部更均匀、边界形状更准确、特征细节更清晰,而且使计算效率及粒子群的收敛精度得到提高。

Thresholding based on improved 2-D exponential entropy and chaotic 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 exponent entropy threshold selection method based on gray level—gradient two-dimensional histogram is proposed.The formulas for threshold selection of exponential entropy based on the improved two-dimensional histogram are derived.The chaotic particle swarm optimization algorithm is used to search the best threshold and the computing cost of fitness function in iteration is reduced using recursion.The experimental results show that compared with the existing corresponding algorithm,the proposed algorithm not only achieves better segmentation quality which obtains uniform regions,accurate borders and clear details of features.The computation efficiency and convergence property of the particle swarm algorithm are improved.