图像清晰度评价函数是聚焦恢复深度法(Depth from Focus,DFF)实现三维形貌测量的核心,直接决定了深度方向的测量精度。文中提出了一种基于高频方差熵的图像清晰度评价函数,与常用函数对比了清晰度比率、灵敏度因子两个定量指标,结果表...图像清晰度评价函数是聚焦恢复深度法(Depth from Focus,DFF)实现三维形貌测量的核心,直接决定了深度方向的测量精度。文中提出了一种基于高频方差熵的图像清晰度评价函数,与常用函数对比了清晰度比率、灵敏度因子两个定量指标,结果表明所提函数优于常用函数。通过对所提函数获得的清晰度评价曲线进行高斯曲线拟合,实现了深度方向聚焦位置的精确计算。对文中方法开展了聚焦重复性与标准台阶高度测量测试,重复性聚焦实验的测量标准差为2.82μm,台阶高度测量标准差为12μm,验证了文中方法用于高精度非接触三维测量的可行性。展开更多
A two-step method for pose estimation based on five co-planar reference points is studied. In the first step, the pose of the object is estimated by a simple analytical solving process. The pixel coordinates of refere...A two-step method for pose estimation based on five co-planar reference points is studied. In the first step, the pose of the object is estimated by a simple analytical solving process. The pixel coordinates of reference points on the image plane are extracted through image processing. Then, using affine invariants of the reference points with certain distances between each other, the coordinates of reference points in the camera coordinate system are solved. In the second step, the results obtained in the first step are used as initial values of an iterative solving process for gathering the exact solution. In such a solution, an unconstrained nonlinear optimization objective function is established through the objective functions produced by the depth estimation and the co-planarity of the five reference points to ensure the accuracy and convergence rate of the non-linear algorithm. The Levenberg-Marquardt optimization method is utilized to refine the initial values. The coordinates of the reference points in the camera coordinate system are obtained and transformed into the pose of the object. Experimental results show that the RMS of the azimuth angle reaches 0.076° in the measurement range of 0°-90°; the root mean square （RMS） of the pitch angle reaches 0.035° in the measurement range of 0°-60°; and the RMS of the roll angle reaches 0.036° in the measurement range of 0°-60°.展开更多
文摘图像清晰度评价函数是聚焦恢复深度法(Depth from Focus,DFF)实现三维形貌测量的核心,直接决定了深度方向的测量精度。文中提出了一种基于高频方差熵的图像清晰度评价函数,与常用函数对比了清晰度比率、灵敏度因子两个定量指标,结果表明所提函数优于常用函数。通过对所提函数获得的清晰度评价曲线进行高斯曲线拟合,实现了深度方向聚焦位置的精确计算。对文中方法开展了聚焦重复性与标准台阶高度测量测试,重复性聚焦实验的测量标准差为2.82μm,台阶高度测量标准差为12μm,验证了文中方法用于高精度非接触三维测量的可行性。
基金supported by the Important National Science & Technology Specific Project(No.2009ZX04014- 092)
文摘A two-step method for pose estimation based on five co-planar reference points is studied. In the first step, the pose of the object is estimated by a simple analytical solving process. The pixel coordinates of reference points on the image plane are extracted through image processing. Then, using affine invariants of the reference points with certain distances between each other, the coordinates of reference points in the camera coordinate system are solved. In the second step, the results obtained in the first step are used as initial values of an iterative solving process for gathering the exact solution. In such a solution, an unconstrained nonlinear optimization objective function is established through the objective functions produced by the depth estimation and the co-planarity of the five reference points to ensure the accuracy and convergence rate of the non-linear algorithm. The Levenberg-Marquardt optimization method is utilized to refine the initial values. The coordinates of the reference points in the camera coordinate system are obtained and transformed into the pose of the object. Experimental results show that the RMS of the azimuth angle reaches 0.076° in the measurement range of 0°-90°; the root mean square （RMS） of the pitch angle reaches 0.035° in the measurement range of 0°-60°; and the RMS of the roll angle reaches 0.036° in the measurement range of 0°-60°.
