准确、快速地提取结构光条纹中心是三维测量系统中的关键问题。针对现存的结构光条纹中心提取精度与速度之间的矛盾,提出一种全新的基于海森(Hessian)矩阵与区域增长相结合的激光条纹中心提取方法。采用自适应阈值法提取图像的感兴趣区域,利用灰度值最大法确定像素级条纹中心的初始位置;利用Hessian矩阵求取初始点法线方向上的亚像素级光条中心点;将光条中心点作为种子点进行区域增长迭代运算,从而精确提取条纹中心。区域增长算法解决了传统方法中存在的大量高斯卷积运算的问题,提高了条纹中心的提取速度。实验结果表明,该算法提取的条纹中心准确度高,满足三维测量系统中实时在线的要求。该算法的均方差相比于灰度重心法降低了2.02 pixel,提取速度相比于Steger法提高了40倍。

The accurate and fast extraction of structured light stripe centers is a key problem in a three-dimensional (3D) measurement system. Aiming at the existing contradiction between extraction precision and speed of structured light stripe centers, a novel laser stripe center extraction method is proposed based on the Hessian matrix and the regional growth. First, the adaptive threshold method is used to extract the region of interest from the images, and the initial position of the pixel-level stripe center is determined by the maximum value of the gray value. Second, the sub-pixel-level strip center point in the normal direction of the initial point is obtained by the Hessian matrix. Finally, the strip center is used as a seed point for the regional growth iteration operation and thus the stripe center is accurately extracted. In the regional growth algorithm, the problem of a large number of Gaussian convolutional operations in the traditional method is solved, and the extraction speed of the stripe center is increased. The experimental results show that the stripe center extracted by the proposed algorithm has a high accuracy and the real-time online requirements of the 3D measurement system is satisfied. The mean square error (RMS) of this algorithm is reduced by 2.02 pixel compared with that of gray-gravity algorithm, and the extraction speed is 40 times higher than that of Steger algorithm.