基于CPSO的二维Otsu图像分割法

2D Otsu Image Segmentation Method Based on CPSO

二维Otsu方法同时考虑了图像的灰度信息和像素间的空间邻域信息,图像分割效果好但算法计算量较大。针对上述情况,提出一种基于混沌粒子群优化算法(CPSO)的策略,将其用于二维Otsu方法中,并与标准粒子群优化算法(SPSO)进行仿真实验对比。实验结果表明,该方法可以提高分割速度,克服SPSO的缺点,图像分割结果较理想。

2D Otsu method considers both the gray information and spatial neighbor information between pixels in the image, so it is efficient but with larger computing load. This paper proposes a strategy based on Chaos Particle Swarm Optimization（CPSO）, applies it into 2D Otsu method, and makes simulation experiments to compare the method and Standard PSO（SPSO）. Experimental results show that this method can increase the speed of segmentation, overcome the disadvantage of SPSO, and gain the ideal result of the image segmentation.