A fast method of extracting the laser light bar's centerline in the ice model

Kang Hanyu; Liu Guihua; Wang Bin; Niu Qian

doi:10.11729/syltlx20170058

Volume 31 Issue 5

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2017 > 31(5): 81-87

Kang Hanyu, Liu Guihua, Wang Bin, et al. A fast method of extracting the laser light bar's centerline in the ice model[J]. Journal of Experiments in Fluid Mechanics, 2017, 31(5): 81-87. doi: 10.11729/syltlx20170058

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A fast method of extracting the laser light bar's centerline in the ice model

doi: 10.11729/syltlx20170058

Kang Hanyu^{1
,
,},
Liu Guihua¹,
Wang Bin^{2, 3},
Niu Qian¹

1.
School of Information Engineering, University of Southwest Science and Technology, Mianyang Sichuan 621000, China
2.
Facility Design and Instrumentation Institute, China Aerodynamics Research and Development Center, Mianyang Sichuan 621000, China
3.
State Key Loboratory of Aerodynamics, China Aerodynamics Research and Development Center, Mianyang Sichuan 621000, China

Received Date: 2017-05-10
Rev Recd Date: 2017-07-17
Publish Date: 2017-10-25

Abstract

Abstract

The laser centerline extraction is an important step in the measurement of the ice cross sectional profile, and the extraction accuracy directly affects the accuracy of the measurement system. Especially the ice on the structural light transmission is serious, the center line extraction algorithm is put forward a higher requirement. A fast centerline extraction method based on frequency domain transform is proposed to extract the center line in the three-dimensional measurement of ice. First, the image is subjected to noise reduction based on the technique of Block-matching and 3D filtering. Second, in order to reduce the computational complexity, the visual significance of the image is calculated by using the quaternion Fourier transform of phase spectrum, and then extract the optical band's region. Third, obtain the gradient map and convert to the frequency domain space, according to the image spectrum characteristics, we can obtain the center line position. The experimental results show that the algorithm is 28.57FPS, and the accuracy of ice contour reconstruction is 0.017mm.
- icing model,
- image processing,
- significant figure,
- centerline extraction,
- frequency domain transform

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References(17)

References

[1]	Lynch F T, Khodadoust A. Effects of ice accretions on aircraft aerodynamics[J]. Progress in Aerospace Sciences, 2001, 37(8):669-767. doi: 10.1016/S0376-0421(01)00018-5
[2]	巫瑞锐. 结冰力学特性的理论与实验研究[D]. 南京: 南京航空航天大学, 2014. Wu R R. Research on theories and experiments of mechanical properties of impact ice[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2014.
[3]	范洁川, 于涛.飞机结冰风洞试验模拟研究[J].实验流体力学, 2007, 21(1):1-7. http://www.syltlx.com/CN/abstract/abstract9525.shtml Fan J C, Yu T. A study of simulation for airplane icing tests in icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics, 2007, 21(1):1-7. http://www.syltlx.com/CN/abstract/abstract9525.shtml
[4]	易贤. 飞机积冰的数值计算与积冰试验相似准则研究[D]. 绵阳: 中国空气动力研究与发展中心, 2007. Yi X. Numerical computation of aircraft icing and study on icing test scaling law[D]. Mianyang:China Aerodynamics Research and Development Center, 2007.
[5]	Gong X L, Bansmer S. 3-D ice shape measurements using mid-infrared laser scanning[J]. Optics Express, 2015, 23(4):4908-4926. doi: 10.1364/OE.23.004908
[6]	王斌, 刘桂华, 张利萍, 等.基于线结构光的冰横截面轮廓测量[J].实验流体力学, 2016, 30(3):14-20. http://www.syltlx.com/CN/abstract/abstract10928.shtml Wang B, Liu G H, Zhang L P, et al. Ice cross sectional profi-le measurement based on line structured light[J]. Journal of Experiments in Fluid Mechanics, 2016, 30(3):14-20. http://www.syltlx.com/CN/abstract/abstract10928.shtml
[7]	Tsai R Y. An efficient and accurate camera calibration technique for 3D machine vision[C]//Proceeding of IEEE Conference on Computer Vision & Pattern Recognition, 1986:364-374.
[8]	Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334. doi: 10.1109/34.888718
[9]	Zhou F, Zhang G. Complete calibration of a structured light vision sensor through planar target of unknown otientations[J]. Image and Vision Computing, 2005, 23(1):59-67. doi: 10.1016/j.imavis.2004.07.006
[10]	Saeed K, Dzki M, Rybnik M, et al. A universal algorithm for image skeletonization and a review of thinning thchniques[J]. International Journal of Applied Mathmatics & Computer Science, 2010, 20(2):317-335. https://www.degruyter.com/view/j/amcs.2010.20.issue-2/v10006-010-0024-4/v10006-010-0024-4.xml
[11]	苏小勤, 熊显名.快速线结构光中心线提取算法[J].计算机应用, 2016, 36(1):238-242. doi: 10.11772/j.issn.1001-9081.2016.01.0238 Su X Q, Xiong X M. High-speed method for extracting center of line structured light[J]. Journal of Computer Applications, 2016, 36(1):238-242. doi: 10.11772/j.issn.1001-9081.2016.01.0238
[12]	吴家勇.基于梯度重心法的线结构光中心亚像素提取方法[J].中国图像图形学报, 2009, 14(7):1354-1360. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200907022.htm Wu J Y. Method of linear structured light sub-pixel center position extracting[J]. Journal of Image and Graphics, 2009, 14(7):1354-1360. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB200907022.htm
[13]	Steger C. Extracting curvilinear structures:a differential geometric approach[J]. Computer Vision-ECCV'96 Lecture Notes in Computer Science, 1996, 1064:630-641. doi: 10.1007/BFb0015518
[14]	Steger C. An unbiased detector of curvilinear structures[J]. IEEE Transactions on Pattern Analysis and machine Intelligence, 1998, 20(2):113-125. doi: 10.1109/34.659930
[15]	Dabov K, Foi A, Egiazarian K. Image denoising with block-matching and 3D filtering[J]. The International Society for Optical Engineering, 2006, 6064:354-365. http://www.cs.tut.fi/~foi/3D-DFT/
[16]	汪成. 图像显著区域检测算法研究[D]. 南京: 南京信息工程大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10300-1016197870.htm Wang C. Research of saliency region detection of images[D]. Nanjing:Nanjing University of Information Science & Technology, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10300-1016197870.htm
[17]	Itti L, Koch C, Niebur E. A model of saliency based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1998, 20(11):1254-1259. http://ieeexplore.ieee.org/abstract/document/730558/