摘要
针对目前图像分割方法较难精确、快速地实现图像分割的问题,提出一种控制活动轮廓演化的快速图像分割方法.首先用外部能量与内部能量加权和作为曲线能量函数,用封闭曲线外部与内部能量建立活动轮廓波模型;然后用最优路径移动更新曲线能量,获取所需图像分割目标;最后引入粒子群优化算法获取全部初始轮廓点的最优控制点,根据最优控制点控制活动轮廓演化达到实现目标图像准确分割的目的.实验结果表明,该方法的图像分割精度明显高于目前典型的图像分割算法,提高了图像分割的抗噪性能及图像分割速度.
Aiming at the problems that current image segmentation methods were difficult to achieve accurate and fast image segmentation, we proposed a fast image segmentation method for controlling the evolution of active contour. Firstly, the weighted sum of external energy and internal energy was used as the curve energy function, and the active contour wave model was established by using the external and internal energy of closed curve. Secondly, the curve energy was updated by moving the optimal path to obtain the required image segmentation target. Finally, the particle swarm optimization algorithm was introduced to obtain the optimal control points of all the initial contour points, and the goal of accurate segmentation of the target image was achieved by controlling the evolution of the active contour according to the optimal control points. The experimental results show that the image segmentation accuracy of the method is significantly higher than that of the current typical image segmentation method, which improves the anti noise performance and the speed of image segmentation.
作者
金晓民
张丽萍
李慧静
JIN Xiaomin;ZHANG Liping;LI Huijing(Institute of Transportation,Inner Mongolia University,Hohehot 010070,China;College of Computer&Information Engineering,Inner Mongolia Normal University,Hohehot 010022,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2020年第3期639-644,共6页
Journal of Jilin University:Science Edition
基金
内蒙古自治区自然科学基金(批准号:2018MS06009).
关键词
粒子群优化算法
活动轮廓波模型
图像分割
能量函数
适应度函数
particle swarm optimization algorithm
active contour wave model
image segmentation
energy function
fitness function
