结合PCA与HEIV的椭圆目标检测算法  

Ellipse detection method based on PCA and HEIV model

作　　者：韩建栋[1] 温静[1] 

机构地区：[1]山西大学计算机与信息技术学院、计算智能与中文信息处理教育部重点实验室,太原030006

出　　处：《计算机工程与应用》2014年第9期1-4,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61201453);高等学校博士学科点专项科研基金(No.20121401120015);北京市工程研究中心“光电信息与仪器”开放研究课题(No.BJGC20110016);山西省自然科学基金(No.2012011014-4);山西省教育厅专项(No.20110002)

摘　　要：提出结合主元变换与异方差变量含误差模型的椭圆识别与定位方法。根据椭圆长轴对应于椭圆主元方向的特点,利用主元变换法将目标边缘数据变换到主元坐标系,给出新的椭圆轮廓度误差评定方法,将变换后数据点集的椭圆轮廓度误差作为椭圆识别的依据,采用基于异方差变量含误差模型的拟合算法获取椭圆的中心坐标。该方法将任意椭圆转化为标准型椭圆,简化了识别过程,考虑到椭圆数据点的异方差特性,提高了椭圆的定位精度,在噪声方差为0.05情况下,定位精度小于0.04 pixel。A method of ellipse detection and location based on Principal Components Analysis（PCA）and Heteroscedastic Errors-in-Variable（HEIV）is proposed. According to the fact that major axis of an ellipse is the direction of principal com-ponents, the data points are projected on the coordinate frame of principal axis using PCA. A new evaluation method for elliptical profile error is proposed, and the ellipse objects are recognized according to the elliptical profile error of the data points after translation. An ellipse fitting method based to the HEIV model is applied to realizing the precision locating of the ellipse objects. The method simplifies the process of ellipse detection by translating arbitrary ellipse to standard one, and improves the location precision in view of the heteroscedastic performance of data points. The location precision is less than 0.04 pixel when the noise variance is 0.05.

关 键 词：机器视觉 椭圆识别 主元分析 轮廓度误差 异方差变量含误差 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]