摘要
传统活动轮廓波模型在分割图像时,常常会出现曲线溢出的情况,为了提高图像分割效果,设计了粒子群算法和活动轮廓波模型的图像分割方法。采用粒子群优化算法建立活动轮廓波模型的能量最小化控制点泛化函数,使用粒子群优化算法搜索泛化函数最优值,通过能量最小化控制点避免改进活动轮廓波模型分割图像出现局部最佳陷阱,避免分割图像出现边缘凹凸性问题。经实验分析,该图像分割方法的图像误分割率低,分割时间短,且抗噪性能强,具有极佳的图像分割效果。
The traditional active contour wave model often appears curve overflow when segmenting image.In order to improve the effect of image segmentation,particle swarm optimization algorithm and active contour wave model are designed.Particle swarm optimization(PSO)algorithm is used to establish the generalization function of energy minimization control point of active contour wave model,and PSO algorithm is used to search for the optimal value of the generalization function.Through the energy minimization control point,the local optimal trap of the improved active contour wave model image segmentation can be avoided,and the concave convex problem of the edge of the segmentation image can be avoided.Through experimental analysis,the image segmentation method in this paper has the advantages of low false segmentation rate,short segmentation time,strong anti noise performance and excellent image segmentation effect.
作者
吴毅强
Wu Yiqiang(Guangzhou Huali Science and TechnologyVocational College,Guangzhou 511325,China)
出处
《国外电子测量技术》
2020年第7期47-51,共5页
Foreign Electronic Measurement Technology
基金
广东省特色重点学科电子商务建设项目(TSZDXK201601)资助。
关键词
粒子群
活动轮廓波
图像分割
泛化函数
particle swarm
activities outline of the wave
images segmentation
generalization function
