Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each sub...Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.展开更多
由于镀金回转体工件的特殊几何特征和尺寸限制,快速准确地获取其全表面图像存在困难。本文提出了一种基于自适应亮度校正的全表面成像方法。首先,为了恢复低亮度区域信息,提出一种自适应调整图像亮度的校正算法,在全局亮度映射预调整后...由于镀金回转体工件的特殊几何特征和尺寸限制,快速准确地获取其全表面图像存在困难。本文提出了一种基于自适应亮度校正的全表面成像方法。首先,为了恢复低亮度区域信息,提出一种自适应调整图像亮度的校正算法,在全局亮度映射预调整后,使用导向滤波器替代传统的高斯滤波器进行图像局部对比度多尺度增强,同时保护划痕和边缘等特征。其次,设计了一种基于ROI(Region of Interest)自适应裁切的图像拼接方法,通过HSV颜色空间下阈值分割和单应矩阵估计提取有效区域,降低图像拼接时由曲面投影失真和视差引起的干扰,并提高算法的运行速度。实验结果表明:本文的亮度校正算法能改善图像特征亮度不一致情况,使得图像配准平均反向投影误差降低约50%,多图像拼接算法速度达1.25幅/s。相比Autostitch、LPC等经典算法,本文算法在精度和效率上都具有明显优势,适用于工业环境中回转体工件的全表面图像获取及缺陷检测。展开更多
基金Supported by "973" National Fundamental Research Program (51332)
文摘Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.
文摘由于镀金回转体工件的特殊几何特征和尺寸限制,快速准确地获取其全表面图像存在困难。本文提出了一种基于自适应亮度校正的全表面成像方法。首先,为了恢复低亮度区域信息,提出一种自适应调整图像亮度的校正算法,在全局亮度映射预调整后,使用导向滤波器替代传统的高斯滤波器进行图像局部对比度多尺度增强,同时保护划痕和边缘等特征。其次,设计了一种基于ROI(Region of Interest)自适应裁切的图像拼接方法,通过HSV颜色空间下阈值分割和单应矩阵估计提取有效区域,降低图像拼接时由曲面投影失真和视差引起的干扰,并提高算法的运行速度。实验结果表明:本文的亮度校正算法能改善图像特征亮度不一致情况,使得图像配准平均反向投影误差降低约50%,多图像拼接算法速度达1.25幅/s。相比Autostitch、LPC等经典算法,本文算法在精度和效率上都具有明显优势,适用于工业环境中回转体工件的全表面图像获取及缺陷检测。
