摘要
用极形态算子优化分形模型,提出了一种复杂背景下的扩展目标分割新算法。首先分析极坐标系中可变尺度形态结构算子的旋转缩放特性,并以此为基础进行极形态运算,再利用最小二乘法提取分形尺度误差进行二值化,然后进行边界跟踪,依据先验知识抑制背景团块,最后保留并填充面积最大的目标团块。仿真实验证明能够有效分割复杂背景下的扩展目标,并较好地保留了目标形状特征。
Using polar coordinates to optimize the fractal model, a novel segmentation algorithm is proposed to deal with extended target under complex environment. First, analyses is introduced for characteristics of morphology operator with variable algebra structures in polar coordinates, corresponding the dilating operations progress to construct a series of covering surfaces. Sceondly the fraetal feature, named Scale Error, is extraeted by the least square method. Then eroding filters eliminate clutter texture. After the course of edge linking, the region with the biggest area is preserved as a target based on transcendental knowledge. Abundance experiments under complex environment test its validity and the contour of extended target is kept intact and perfect.
出处
《强激光与粒子束》
EI
CAS
CSCD
北大核心
2007年第2期229-233,共5页
High Power Laser and Particle Beams
基金
国家863计划项目资助课题
关键词
扩展目标
形态学
极坐标
分形
边界跟踪
Extended target
Morphology
Polar coordinates
Fractal
Edge linking
