基于视觉的刀具特征点识别及定位算法  

Feature point identification of tools and positioning algorithm based on vision

作　　者：刘今越[1] 刘子欣 李洋[1] LIU Jinyue;LIU Zixin;LI Yang(School of Mechanical Engineering,Hebei University of Technology,Tianjin 300310,China)

机构地区：[1]河北工业大学机械工程学院

出　　处：《传感器与微系统》2020年第1期140-142,共3页Transducer and Microsystem Technologies

基　　金：河北省教育厅重点资助项目(ZD2018246)

摘　　要：为满足电荷耦合器件(CCD)相机投影测量中定位刀具特征点快速、高精度的识别要求,提出了一种改进k-余弦曲率的识别算法。采用一种固定支持领域的夹角曲率计算方法,在获取曲线转动率的同时记录其弯曲方向。将常见的刀具轮廓特征点分为LL-C型、AL-T型和AL-C型三种形式,通过特征点附近的曲率特征分析,实现对特征点的精确定位。为验证该算法的有效性,利用标准轮廓样板进行仿真实验,其结果表明,该方法对刀具特征点的识别及定位误差低于2个像素。In order to meet the fast and high-precision recognition requirements of feature points of positioning tool in CCD camera projection measurement,an improved k-cosine curvature recognition algorithm is proposed.An angle curvature calculation method by fixing support field is used in the algorithm to obtain the rotation rate and the bending direction of the curve.Firstly,the tool contour feature points are classified into LL-C type,AL-T type and AL-C type.Then,the curvature characteristics around different feature points are analyzed to achieve the accurate positioning of feature points.Finally,in order to verify the effectiveness of the algorithm,simulation experiments are conducted by standard contour model,the results show that the recognition and positioning errors of the method on tool feature point are less than 2 pixels.

关 键 词：电荷耦合器件(CCD)相机 刀具测量 夹角曲率 刀具特征点 

分 类 号：TH741[机械工程—光学工程] TP212[机械工程—仪器科学与技术]