摘要
针对激光拼焊焊缝结构光光纹图像畸变小、特征点不明显且易受噪声干扰的特点,提出了一种基于改进的曲率尺度空间的特征点识别方法.该方法首先采用较高尺度的高斯卷积模板,对光纹曲线进行卷积滤波并使用自适应K-余弦法计算轮廓上每一点的曲率并选取特征点;然后在小尺度高斯模板下跟踪定位大尺度模板下获得的特征点候选点,以获得小尺度模板下光纹曲线特征点的准确位置.在光纹曲线的离散曲率计算过程中,考虑了支持区域对曲率计算的影响,提出了一种自适应K-余弦法,与传统的离散曲率计算公式相比,自适应K-余弦法具有更好的抗干扰能力.
Feature point detection of laser stripe is the key technology for automatic seam quality inspection.Due to small aberrance and sensitive to noise of laser stripe for tailored blanks laser welding,the traditional feature point extraction methods are difficult to obtain accurate feature points.On the basis of analyzing the image features of laser stripe,an improved multi-scale feature point detection method was proposed based on curvature scale space(CSS) technique.Firstly,a small-scale Gaussian template is used to smooth the centerline of laser stripe.Secondly,the improved CSS adopts an adaptive K-cosine algorithm,which has a dynamic region of support and excellent noise suppression performance to calculate the curvature of centerline smoothed by large-scale Gaussian template.Thirdly,the local extrema of the curvature extracted by a threshold T are used to candidate feature points.Lastly,the candidate feature points are refined in small-scale Gaussian template.The results showed that the improved CSS algorithm has better robust and anti-interference than the that of CSS algorithm.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2013年第7期93-96,117-118,共4页
Transactions of The China Welding Institution
关键词
激光拼焊
结构光
特征点识别
曲率尺度空间
tailored blanks laser welding
structured light
feature point detection
curvature scale space
