提出一种新的线结构光中心提取算法。该算法从光条区域与背景区域具有高对比度这一特性出发，基于最大极值稳定区域算法（maximally stable extremal regions, MSER）对光条有效区域进行快速高效的提取。基于只对光条有效区域进行扫描的思想，避免了传统算法对图像冗余信息扫描过程中易受噪声影响及检测效率较低的不足。此外，针对灰度重心法存在因激光条纹横截面参与计算数量不同而导致光条中心存在偏移误差的不足，提出基于开窗思想的灰度重心法对基于MSER所检测的有效光条区域进行中心提取。试验结果表明: 该算法能够实现对光条中心的快速提取，比Steger算法提速将近19倍，且具有较高抗噪性。

提出一种新的快速激光条纹中心提取算法,该算法具有较好的抗噪声或冗余点能力。该算法在传统重心算法的基础上结合轮廓跟踪算法,根据激光条纹在图像中的分布特点,通过阈值轮廓跟踪算法避免了对图像中不包含激光条纹区域的扫描,以此提高了提取速度,不用对整幅图像完成一次扫描即可计算出激光光条中心。所提算法具有复杂度低、计算简单、程序运行时间少等优点。实验结果表明:该算法能够实现对光条中心的快速提取,比Steger算法提速将近70.37倍,比传统重心算法提速将近4.48倍;对光条图像增加噪声(冗余)点后发现,所提算法具有优良的抗噪效果。

A monocular vision-based pose measurement system is provided for real-time measurement of a three-degree-of-freedom (3-DOF) air-bearing test-bed. Firstly, a circular plane cooperative target is designed. An image of a target fixed on the test-bed is then acquired. Blob analysis-based image processing is used to detect the object circles on the target. A fast algorithm (FCCSP) based on pixel statistics is proposed to extract the centers of object circles. Finally, pose measurements can be obtained when combined with the centers and the coordinate transformation relation. Experiments show that the proposed method is fast, accurate, and robust enough to satisfy the requirement of the pose measurement.