结合斜率差和动态融合的自适应轮廓分段

赵辉; 丰懿阳; 尹小恰; 陶卫

doi:doi:10.3788/ope.20182603.0680

光学精密工程, 2018, 26 (3): 680, 网络出版: 2018-04-25

结合斜率差和动态融合的自适应轮廓分段

A self-adaptive curve-segment method combining slope difference and dynamic merge

论文大纲

赵辉丰懿阳尹小恰陶卫

作者单位

上海交通大学仪器科学与工程学系, 上海 200240

自适应轮廓分段斜率差自适应平滑动态融合 self-adaptive curve-segment slope difference self-adaptive smooth dynamic merge

摘要

为了使用圆弧和直线重建数字轮廓曲线, 提出了一种融合了基于斜率差判断断点方法和区段动态融合的数字轮廓曲线自适应分段的方法。首先, 利用直线拟合计算轮廓曲线上每点的前后方向的斜率差, 求取斜率差的过程使用了自适应长度拟合窗口来平衡精度和速度的关系。然后, 将自适应平滑方法应用在方向斜率差曲线上。基于平滑后的斜率差曲线, 提取其中间断点作为待选区段分段点。最后, 使用基于可视化误差判定的区段动态融合, 来从待选分段点中选出一个可视化误差最小的分段方案作为最后的分段结果。仿真测试的结果显示这种分段方法的断点定位误差小于1%。以轴承油沟轮廓为实际测试用例的实验结果证实了这种自适应轮廓分段方法在实际应用中的可行性。

Abstract

In order to reconstruct the digital contour curve with arc and straight line, a method was proposed to integrate the segmentation of digital contour curve based on the slope difference judgement method and the dynamic segment merge method. First, the slope difference between the front and back directions of each point on the contour curve was calculated using straight line fitting. The process of obtaining the slope difference used the adaptive length fitting window to balance the relationship between the precision and the velocity. Then the adaptive smoothing method was applied to the direction slope difference curve. Based on the smooth slope of the slope curve, the intermittent point was extracted as the selected section segmentation point. Finally, a dynamic merge algorithm based on the perceptual error was used to select a segmentation scheme with the smallest perceptual error from the selected segments as the final result. The simulation results show that the error of this segmentation method is less than 1%. The experimental results of the contours of bearing oil groove as the actual test cases show the feasibility of this adaptive contour segmentation method in practical application.

参考文献

[1] 王宁, 段振云, 赵文辉, 等. 视觉检测中齿轮外轮廓分段方法研究［J］. 组合机床与自动化加工技术, 2016(4): 117-120.

WANG N, DUAN ZH Y, ZHAO W H, et al.. Research on method of gear outer contour segmentation in vision measurement［J］. Modular Machine Tool & Automatic Manufacturing Technique, 2016(4): 117-120. (in Chinese).

[2] ROSENFELD A, JOHNSTON E. Angle detection on digital curves［J］. IEEE Transactions on Computers, 1973, 22(9): 875-878.

[3] NGO P, NASSER H, DEBLED-RENNESSON I, et al.. Adaptive tangential cover for noisy digital contours［C］. Proceedings of the 19th IAPR International Conference on Discrete Geometry for Computer Imagery, Springer, 2016: 439-451.

[4] HORNG J H. An adaptive smoothing approach for fitting digital planar curves with line segments and circular arcs［J］. Pattern Recognition Letters, 2003, 24(1-3): 565-577.

[5] HORNG J H, LI J T. A dynamic programming approach for fitting digital planar curves with line segments and circular arcs［J］. Pattern Recognition Letters, 2001, 22(2): 183-197.

[6] SARKAR B, SINGH L K, SARKAR D. Approximation of digital curves with line segments and circular arcs using genetic algorithms［J］. Pattern Recognition Letters, 2003, 24(15): 2585-2595.

[7] 王露, 邢宗义, 陈双, 等. 基于自动提取分段点的车轮外形轮廓拟合方法［J］. 广西大学学报(自然科学版), 2017, 42(2): 618-626.

WANG L, XING Z Y, CHEN SH, et al.. A wheel profile fitting method based on automatically extracting subsection points［J］. Journal of Guangxi University (Natural Science Edition), 2017, 42(2): 618-626. (in Chinese)

[8] 张冉, 张旭, 章海波. 截面数据精确分段方法研究［J］. 轻工机械, 2014, 32(6): 70-73, 77.

ZHANG R, ZHANG X, ZHANG H B. Research on accurate segmentation method of sectional data［J］. Light Industry Machinery, 2014, 32(6): 70-73, 77. (in Chinese)

[9] RATTARANGSI A, CHIN R T. Scale-based detection of corners of planar curves［C］. Proceedings of the 10th International Conference on Pattern Recognition, IEEE, 1990: 923-930.

[10] 乔宇, 黄席樾, 柴毅, 等. 基于自适应直线拟合的角点检测［J］. 重庆大学学报, 2003, 26(2): 29-31.

QIAO Y, HUANG X Y, CHAI Y, et al.. Corner point detection based on adaptive line approximation［J］. Journal of Chongqing University, 2003, 26(2): 29-31. (in Chinese)

[11] SAINT-MARC P, CHEN J S, MEDIONI G. Adaptive smoothing: a general tool for early vision［C］. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 1989: 618-624.

[12] YIN X Q, TAO W, FENG Y Y, et al.. Laser stripe extraction method in industrial environments utilizing self-adaptive convolution technique［J］. Applied Optics, 2017, 56(10): 2653-2660.

赵辉, 丰懿阳, 尹小恰, 陶卫. 结合斜率差和动态融合的自适应轮廓分段[J]. 光学精密工程, 2018, 26(3): 680. ZHAO Hui, FENG Yi-yang, YIN Xiao-qia, TAO Wei. A self-adaptive curve-segment method combining slope difference and dynamic merge[J]. Optics and Precision Engineering, 2018, 26(3): 680.

结合斜率差和动态融合的自适应轮廓分段

关于本站 Cookie 的使用提示

全站搜索

结合斜率差和动态融合的自适应轮廓分段

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索