Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation

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Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation

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[1]Zhu Xinglong,Cao Yu,Ma Qian,et al.Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation[J].Journal of Zhengzhou University (Engineering Science),2021,42(04):63-69.[doi:10.13705/j.issn.1671-6833.2021.04.024]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42 Number of periods: 2021 04 Page number: 63-69 Column: Public date: 2021-07-30

Title:: Robustness Research of Center Coordinate Value for Speckle Contour and Denoising Algorithm Implementation

Author(s):: Zhu Xinglong; Cao Yu; Ma Qian; Yin Yiyao;; School of Mechanical Engineering, Yangzhou University;

Keywords:: speckle contour; de-noising algorithm; robustness; ellipse fitting

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2021.04.024

Abstract:: The combination of monocular vision and laser spot could measure the depth information.The laser spot was irradiated on the surface of the object to be measured,after imaging by CCD camera,it would be generally elliptical spot,the accuracy of the center coordinate value (CCV)of the speckle would effect of the depth information.Due to the influence of surface roughness,surface bumps,pits and other defects to be measured,the edge of the imaging speckle was scattered,resulting in noise in the contour after image processing.When this data with contour noise was used to fit the spot center,the CCV of the speckle would be affected by the contour noise.The principle of depth information measurement by monocular vision and laser point combination was expounded,the causes of speckle contour noise were analyzed,a method to eliminate contour noise based on statistical principle at introverted and extroverted ellipse boundaries was proposed,two-step method was adopted to verify the robustness of the CCV of the proposed algorithm.In the first step,ideal data was adopted,that was,the known speckle ellipse was added with various noise points to verify that the algorithm could converge to the known ellipse center,and the proposed algorithm was superior to other typical algorithms and had better robustness.In the second step,the actual images of different depths of the measured surface were collected by the image measuring system to obtain the CCV of the speckle contour.The relationship model between the depth information of the measured surface and the CCV of the speckle contour was obtained by the fitting method.The comparison between the measured data of different positions of the measured surface and the calculated data of the relationship model showed that the measurement accuracy of the algorithm was better than other algorithms,which indirectly verified that the speckle center of the algorithm after denoising was closer to the theoretical speckle center and has better robustness.

References:: [1] LIU Z Y,QIAO H.Multiple ellipses detection in noisy environments:a hierarchical approach[J].Pattern recognition,2009,42(11):2421-2433.
[2] BAI X Z,SUN C M,ZHOU F G.Splitting touching cells based on concave points and ellipse fitting[J].Pattern recognition,2009,42(11):2434-2446.
[3] KASHIHA M A,BAHR C,OTT S,et al.Automatic monitoring of pig locomotion using image analysis[J].Livestock science,2014,159:141-148.
[4] CHAUDHURI D.A simple least squares method for fitting of ellipses and circles depends on border points of a two-tone image and their 3-D extensions[J].Pattern recognition letters,2010,31(9):818-829.
[5] CHOJNACKI W,BROOKS M J,Van DEN HENGEL A,et al.On the fitting of surfaces to data with covariances[J].IEEE transactions on pattern analysis and machine intelligence,2000,22(11):1294-1303.
[6] DUDA R O,HART P E.Use of the Hough transformation to detect lines and curves in pictures[J].Communications of the acm,1972,15(1):11-15.
[7] NAIR P S,SAUNDERS A T.Hough transform based ellipse detection algorithm[J].Pattern recognition letters,1996,17(7):777-784.
[8] YUEN H K,ILLINGWORTH J,KITTLER J.Detecting partially occluded ellipses using the Hough transform[J].Image and vision computing,1989,7(1):31-37.
[9] JAI JAGANATH BABU J,SUDHA G F.Non-subsampled contourlet transform based image denoising in ultrasound thyroid images using adaptive binary morphological operations[J].IET computer vision,2014,8(6):718-728.
[10] MAI F,HUNG Y S,ZHONG H,et al.A hierarchical approach for fast and robust ellipse extraction[J].Pattern recognition,2008,41(8):2512-2524.
[11] PRASAD D K,LEUNG M K H,CHO S Y.Edge curvature and convexity based ellipse detection method[J].Pattern recognition,2012,45(9):3204-3221.[12] HUO G Y,YANG S X,LI Q W,et al.A robust and fast method for sidescan sonar image segmentation using nonlocal despeckling and active contour model[J].IEEE transactions on cybernetics,2017,47(4):855-872.
[13] SRIDAR P,KUMAR A,LI C Y,et al.Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets[J].IEEE journal of biomedical and health informatics,2017,21(4):1069-1078.
[14] VILLAR S A,De PAULA M,SOLARI F J,et al.A framework for acoustic segmentation using order statistic-constant false alarm rate in two dimensions from sidescan sonar data[J].IEEE journal of oceanic engineering,2018,43(3):735-748.

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Last Update: 2021-08-26