摘要
针对当前主动轮廓模型难实现图像高精度分割的问题,以获得更理想的图像分割结果为目标,提出一种基于改进粒子群优化算法的图像分割方法.首先分析传统主动轮廓模型,指出其存在的局限性;然后建立能量最小化控制点的泛化函数,采用粒子群优化算法对泛化函数的最优值进行搜索,根据所有的能量最小化控制点实现图像分割;最后采用标准图像库与传统图像分割方法进行对比测试.测试结果表明,相对于传统方法,该方法能更精准、快速地分割图像,并有效抑制图像中的噪声干扰,可获得理想的图像分割效果.
Aimimg at the problem that the active contour model was difficult to achieve high precision segmentation of the image,in order to obtain more ideal image segmentation results,the author proposed a new image segmentation method based on improved particle swarm optimization（PSO）algorithm.Firstly,the traditional active contour model was analyzed,and the limitation of its existence was pointed out.Secondly,the objective function of the energy minimization control point was established,the optimal value of the objective function was searched by the particle swarm optimization algorithm,and the image segmentation was realized according to all the energy minimization control points.Finally,the standard image database and the traditional image segmentation method were uesd for comparative test.The test results show that,compared with the traditional method,the proposed method can segment the images more accurately and quickly.It can effectively suppress the noise interference in the images,and obtain ideal image segmentation effect.
作者
刘洋
LIU Yang(Institute of Cloud Computing and Big Data,Henan University of Economics and Law,Zhengzhou 450046,Chin)
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2018年第4期959-964,共6页
Journal of Jilin University:Science Edition
基金
河南省科技攻关项目(批准号:182102210021
182102210022
182102210035)
河南省高等学校重点科研项目(批准号:18A520014
17A520020)
关键词
图像分割
主动轮廓模型
粒子群优化算法
泛化函数
能量最小化
image segmentation
active contour model
particle swarm optimization algorithm
objective function
energy minimization
