基于互相关算法的激光条纹中心提取

Laser Stripe Center Extraction Based on Cross-Correlation Algorithm

线结构光三维测量中,光条中心点提取的准确度和精度直接影响最终测量结果的精确度。针对现有激光条纹中心提取方法抗干扰能力强、稳健性好与计算量大之间的矛盾,提出了一种互相关中心条纹提取方法。利用梯度阈值自动分割出有效光条区域,将赋予不同权值的互相关系数与相对应的光条纹灰度值进行互相关运算,以互相关极大值对应的条纹作为初始光条纹中心,通过曲线拟合的方法对条纹中心进行精确定位。以互相关值大小作为条纹中心点是否存在的评判依据,利用相邻光条中心点间的灰度、位置相似性约束消除噪声影响。实验结果表明,该算法条纹提取精度较高,满足实时性要求。同当前算法相比,互相关算法简单实用、稳健性好、抗噪声能力强,且对断线条纹具有很强的修补能力。

In line structured light vision measurement, the extraction accuracy and precision of the light stripe center point directly affects the final accuracy of the measurement results. The contradiction between high capacity of anti-interference, good robustness and intensive computation exists in optical center extraction algorithm, so a cross-correlation stripe center extraction algorithm is proposed. The gradient threshold is used to separate the effective stripes region, and correlation is applied between cross-correlation coefficient and corresponding gray scale. The stripe with cross-correlation＇s extreme value is selected as the initial center, and then the curve fitting method is used to refine the accurate stripe center. Here the cross-correlation＇s value is used to evaluate whether the stripe center exists or not, and the similarity constraints of gray scale and position in the neighborhood stripes are employed to eliminate the noise. The experimental results show that the algorithm has high stripe extraction accuracy and calculation speed. Compared with current algorithms, it is simple and practical, and also has good robustness and the ability of anti-noise repair on disconnected stripes.