摘要
提出一种基于微粒群优化(PSO)的边界区域粗糙熵的阈值图像分割算法。该算法采用边界粗糙熵作为图像分割的评价标准,利用优化领域的PSO功能把图像分割问题转化为优化问题。实验结果表明,该方法使用PSO算法避免了早期大量熵的计算,相对于分块大小的敏感性较小,得到较好的分割效果,并且能提高计算速度,是一种实用有效的图像分割方法。
The image threshold segmentation algorithm based on the Particle Swarm Optimization（PSO） combined with the rough entropy based on boundary region is presented.The algorithm adopts the rough entropy based on boundary region as the valuation standard of image segmentation and converses image segmentation problem into an optimization problem and fully utilizes PSO function in the optimization field.Experimental results show that the proposed method can not only obtain the perfect performance of segmentation but also greatly improve the speed of computation,it avoids a great deal of entropy calculation for the use of PSO and the sensibility of the algorithm to the partition-size image sub-piece is low,it is a practical and effective method of image segmentation.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第14期228-230,共3页
Computer Engineering
基金
江苏省产业信息化重点基金资助项目(1633000004)
关键词
图像分割
微粒群优化
边界地区
粗糙熵
image segmentation
Particle Swarm Optimization（PSO）
boundary region
rough entropy
