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光纤和波导对准过程中边缘高精度线性拟合 被引量:2

High-precision Linear Fitting of Edges in Alignment of Fiber and Waveguide
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摘要 对已有边缘直线检测算法无法满足波导器件边缘的高精度检测需要。基于最小二乘的基本思想,提出了一种拟合系数收敛的直线逼近方法。首先分析了拟合系数与样本空间线性相关性的关系并根据拟合系数建立了非随机误差的判断模型;然后在非随机误差有限可分且足够大的假设条件下,证明了总体样本空间存在方差小于总体方差且只与随机误差有关的样本子空间;最后基于残差关系提出直线拟合逼近的新方法。实验结果表明,与传统直线检测方法比较,新算法能精确地检测出受损边缘,角度误差小于0.01°。新方法具有拟合精度高,抗边缘噪声影响能力强,拟合系数收敛快的特点,达到微电子,光电子封装等领域高精度线性检测的需要。 High-precision edge detection of fiber and waveguide chip is an important basis for the position and posture adjustment of waveguide alignment. Considering that the conventional edge line detection algorithms can not meet the need of high-precision detection of waveguide devices edge, a new fitting method of linear approximation based on the idea of least squares was introduced, where the coefficient converged to zero. First of all, factor analysis of relationship between the fitting function coefficient and the linear correlation coefficient was undertaken, and a assessment model of non-random error was established. Then, under the hypothesis that non-random errors was assumed to be large enough and was limited in small enough regions, a subspace, which belonged to a general sample space and only have random errors, was proved to have less variance than that of the general sample space. Finally, based on the relationship of residuals of the linear approximation, a new fitting method was proposed. The experiment results showed that the new algorithm accurately detected the linear edge which was damaged and the angle error was less than 0.01 degree. Therefore, the new method has some excellent characteristics, e.g. high precision, anti-impact ability on errors, and fast convergence of fitting coefficients, and it can meet the need of high-precision fields, such as auto micro-electronics and opto-electronics packaging.
作者 阳波 段吉安
出处 《光电工程》 CAS CSCD 北大核心 2011年第11期16-22,共7页 Opto-Electronic Engineering
基金 国家自然科学基金重点项目(50735007) 国家863高技术研究发展技术项目(2007AA04Z344)
关键词 直线检测 波导器件封装 机器视觉 最小二乘 拟合系数 straight-line detection waveguide device packaging machine vision least squares coefficient of fitting
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参考文献12

  • 1Doerr C R, Okamoto. Advances in Silica Planar Light-wave Circuit [J]'. Journal of Lightwave Technology(S0733-8724), 2006, 24(12): 4763-4789.
  • 2Murakawa M, Nosato H, Higuchi T. Automatic Optical Fiber Alignment System Using Genetic Algorithms [J]. Systems and Computers in Japan(S1520-684X), 2004, 10(35): 80-90.
  • 3ZHANG Rong, Frank G Shi. A Novel Algorithm for Fiber-Optic Alignment Automation [J]. Advanced Packaging(S1521-3323), 2004, 27(1): 173-178.
  • 4Jeong S H, Kimb G H, Chac K R. A study on optical device alignment system using ultra precision multi-axis stage [J]( Journal of Materials Processing Technology(S0924-0136), 2007, 187/188: 65-68.
  • 5Sang Hwa Jeong, Gwang Ho Kim, Kyoung Rae Cha. A Study on Automation Program for the Characteristics Improvement of Optical Element Alignment System [J]. Key Engineering Materials(S 1013-9826), 2006, 326/328(1): 305-308.
  • 6Ballard D H. Generalizing the Hough transform to detect arbitrary shapes [J]. Pattern Recognition(S0031-3203), 1981, 13(2): 111-122.
  • 7Angela Yao, Juergen Gall, Luc Van Gool. A Hough transform-based voting framework for action recognition [C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, June 13-18, 2010: 2061-2068.
  • 8ZHENG Da-ming, ZHENG Li-ying, LIU Ji-gang. Advanced Hough Transform Using A Multilayer Fractional Fourier Method [J]. Image Processing, IEEE Transactions on(S1057-7149), 2010, 19(6): 1558-1566.
  • 9张晶,张权,王欣.一种新的基于统计向量和神经网络的边缘检测方法[J].计算机研究与发展,2006,43(5):920-926. 被引量:13
  • 10周云燕,杨坤涛.基于RHT-LSM直线检测方法的研究[J].光电工程,2007,34(1):55-58. 被引量:16

二级参考文献25

  • 1孙即祥.现代模式识别[M].北京:国防科技大学出版社,2001..
  • 2Vapnik V.N..The Nature of Statistical Learning Theory.New York:Springer-Verlag,1995
  • 3Cherkassky V.,Mulier F..Learning From Data-Concepts,Theory and Methods.New York:John Wiley Sons,1998
  • 4Joachims T..Text Categorization with support vector machines:Learning with Many Relevant Features.In:Proceedings of the European Conference on Machine Learning (ECML),1998,137~142
  • 5Guyon I.,Weston J.,Barnhill S..Gene selection for cancer classification using support vector machines.Machine Learning,2002,46(1):389~422.
  • 6Kivinen J.,Smola A.,Williamson R..Online learning with kernels.In:Diettrich T.G.,Becker S.,Ghahramani Z.eds..Advances in Neural Information Processing Systems.Cambridge,MA:MIT Press,2002,785~793
  • 7Ralaivola L.et al..Incremental support vector machine learning:A local approach.In:Proceedings of the International on Conference on Artificial Neural Networks,Vienna,Austria,2001,322~329
  • 8Ruping S..Incremental learning with support vector machines.Dortmund University,Dortmund:Technical Report TR 18,2002
  • 9Martin M..On-line support vector machines for function approximation.Politecnica University,Catalunya,Spain:Technical Report LSI-02-11-R,2002
  • 10Kuh A..Adaptive kernel methods for CDMA systems.In:Proceedings of the International Joint Conference on Neural networks(IJCNN.2001),Washington DC,2001,1404~1409

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