基于微粒群算法的二维最大熵图像分割方法

该文研究了基于二维最大熵的图像分割方法,针对二维最大熵图像分割方法求取阈值时存在的计算复杂、时间长、实用性差等问题,提出了基于微粒群算法的二维最大熵图像分割方法.该方法运用微粒群算法对图像的二维阈值空间进行全局搜索,并将搜索得到的二维熵最大值所对应的点灰度-区域灰度均值对作为阈值进行图像分割.实验结果表明,由于该方法考虑了点灰度和区域灰度均值,且采用了离散的全局搜索算法,所以不仅得到了令人满意的分割效果,而且大大的提高了计算速度,是一种实用有效的图像分割方法.

2-D Maximum Entropy Method of Image Segmentation Based on Particle Swarm Optimization

The 2-D maximum entropy image segmentation method is studied in this paper,for the problems that the method is complex,time-consuming and lack of practicability during evaluating threshold,a 2-D maximum entropy image segmentation method based on particle swarm optimization is presented.The proposed method searches the 2-D space of threshold using particle swarm optimization algorithm,and takes the gray scale value of pixel and the gray scale mean value of region corresponding to the 2-D maximum entropy value in the search space as the threshold for image segmentation.The experiment results indicate that the proposed method can not only obtain the perfect performance of segmentation but also greatly improve the speed of computation due to considering the gray scale value of pixel and the gray scale mean value of region as well as adopting global search algorithm. So it is a practical and effective method of image segmentation.