CAJ | 학술논문

在高分辨率图像中,为获得较高的椭圆目标识别效率与拟合精度,提出了一种利用自动聚类技术优化椭圆识别和拟合的方法.本方法先对原始图像进行降采样;在缩减图的颜色空间中进行kmeans自动聚类并进行原始图像的预分割;经图像连通性分析后,分离出若干目标区域;再利用各目标区域的边界信息拟合椭圆;最后依次经边界长度约束、椭圆方程参数约束、椭圆拟合精度误差指标正确识别出椭圆目标物,并获得较高精度的椭圆方程.实验表明,该方法能够自适应图像质量的较大范围波动,具有较高的识别正确率和拟合精度,同时也兼顾了识别速度,在工业视觉测量领域具有一定的理论和实用价值.
재고분변솔도상중,위획득교고적타원목표식별효솔여의합정도,제출료일충이용자동취류기술우화타원식별화의합적방법.본방법선대원시도상진행강채양;재축감도적안색공간중진행kmeans자동취류병진행원시도상적예분할;경도상련통성분석후,분리출약간목표구역;재이용각목표구역적변계신식의합타원;최후의차경변계장도약속、타원방정삼수약속、타원의합정도오차지표정학식별출타원목표물,병획득교고정도적타원방정.실험표명,해방법능구자괄응도상질량적교대범위파동,구유교고적식별정학솔화의합정도,동시야겸고료식별속도,재공업시각측량영역구유일정적이론화실용개치.
For high-resolution images,in order to get high efficiency and fitting error of ellipse recognition,a method of optimizing ellipse recognition and fitting by automatic clustering is proposed. Down sampling of the original image is the first step;then kmeans automatic clustering and pre-segmentation of the original image in the color space of reduced image is conducted;after image connectivity analysis,target areas are separated and the boundary information of each target area are used to fit the ellipse;the ellipse object is finally recognized correctly by boundary length constraint, elliptic equation parameter constraints and the indicators of ellipse fitting precision error,with a high accuracy elliptic equation obtained.It is proved by experiments that this method could adapt to the wide fluctuation of the quality of images,and has a high recognition correct rate and fitting accuracy.The speed of recognition is taken into account too.This method has theoretical and practical value in the field of industrial vision measurement.