摘要
在自然场景下,受背景纹理、遮挡、光线影响,不容易提取到对象完整的轮廓,为此,提出了一种基于局部轮廓特征的识别方法,该局部轮廓特征是由直线和曲线组成的2相邻轮廓片段特征(2AS)。首先,分析相邻片段之间的夹角、片段的长度和弯曲强度,定义2AS的语义模型;接着,依据2AS之间的相对位置关系定义2AS相互关系模型,分别描述对象的2AS特征和2AS之间的相互关系;然后,使用对象模板的2AS的语义模型与测试图像中的2AS特征进行初步匹配,接着依据对象模板的2AS相互关系模型进行精确匹配;最后,根据对象模板的2AS相互关系模型检测到的成组2AS进行重复性聚类,并根据对象模板的2AS相互关系模型对聚类的对象判决。与使用近似直线片段组成的2AS特征算法的对比实验结果证明,该算法对输电线路中均压环部件的识别具有较高的正确率、较低的误检率和漏检率,从而更为有效地识别均压环部件。
In the natural scenario,it is difficult to extract a complete outline of the object because of background textures,light and occlusion. Therefore an object recognition method based on local contour feature was proposed. Local contour feature of this paper formed by chains of 2-adjacent straight and curve contour segments（ 2AS）. First,the angle of the adjacent segments,the segment length and the bending strength were analyzed,and the semantic model of the 2AS contour feature was defined. Then on the basis of the relative position relation between object＇s 2AS features,the 2AS mutual relation model was defined. Second,the 2AS semantic model of the object template primarily matched with the 2AS features of the test image,then 2AS mutual relation model of object template accurately matched with the 2AS features of the test image. At last,the pairs of 2AS of detected local contour features were obtained and repeatedly grouped,then grouped objects were verified according to the 2AS mutual relation model of object template. The contrast experiment with the 2AS feature algorithm with similar straight-line chains,the proposed algorithm has higher accuracy,low false positive rate and miss rate in the recognition of grading ring,then the method can more effectively recognize the grading ring.
出处
《计算机应用》
CSCD
北大核心
2016年第5期1399-1403,共5页
journal of Computer Applications
基金
2014年国家电网发展项目(169)~~
关键词
对象识别
局部轮廓特征
相邻片断
相互关系模型
object recognition
local contour feature
adjacent segment
mutual relation model
