针对目前机器视觉方法对柔性机构振动进行非接触测量时其结果易受环境干扰等问题，提出一种基于图像二值化的最大类间方差算法的非接触视觉测量方法。首先分析传统二维最大类间方差算法的错分与计算复杂等缺陷，采用二维直方图分块法和可变步长迭代法对传统二维最大类间方差算法进行改进，然后以Lena图像和柔性机械臂为例分别进行了仿真分析和振动位移测量实验验证。结果显示，基于改进二维最大类间方差算法的非接触视觉测量方法显著提高了图像分割效果，分割时间仅为传统二维最大类间方差算法的约10%；以仿真分析得到的柔性机械臂末端振动位移为评判标准进行测量准确性比较，基于改进二维最大类间方差算法的测量结果明显优于传统二维最大类间方差法和压电法，证明了基于图像二值化的最大类间方差算法的柔性机构振动非接触视觉测量方法的可行性和可靠性。
It is known that the non-contact measurements of the flexible mechanism vibration by the machine vision method are susceptible to the environmental interference, therefore, this paper proposes a non-contact measurement method, the maximal between-cluster variance algorithm based on the image binarization. Firstly, due to the drawbacks of the traditional twodimensional maximal between-cluster variance algorithm, such as the misclassification and the complicated computation, the twodimensional histogram partitioning method and the variable step size iteration method are used to improve the traditional twodimensional maximal between-cluster variance algorithm, then the Lena image and the flexible cantilever beam are taken as examples to carry out the simulation analysis and the vibration displacement measurement experiments, respectively. The results show that the non-contact measurement method based on the improved maximal between-cluster variance algorithm significantly improves the image segmentation effect, and the segmentation time is 10% of the traditional maximal between-cluster variance algorithm. With the vibration displacement of the end of the flexible cantilever beam obtained by the simulation as the criterion for the measuring accuracy, the measurement result of the improved two-dimensional maximal between-cluster variance algorithm is found superior to the traditional two-dimensional maximal between-cluster variance method and the piezoelectric method, with feasibility and reliability.
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