Research Papers

Shape Descriptor-Based Local Contour Profile Registration and Measurement for Flexible Automotive Sealing Strips

[+] Author and Article Information
Jianhua Li

Department of Computer Science
and Engineering,
East China University of Science
and Technology,
130 Meilong Road,
Shanghai 200237, China
e-mail: jhli@ecust.edu.cn

Zhengchun Du

School of Mechanical Engineering,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Shanghai 200240, China
e-mail: zcdu@sjtu.edu.cn

Yan Wang

Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
813 Ferst Drive NW,
Atlanta, GA 30332
e-mail: yan.wang@me.gatech.edu

1Corresponding author.

Manuscript received October 24, 2016; final manuscript received February 7, 2018; published online March 16, 2018. Editor: Satyandra K. Gupta.

J. Comput. Inf. Sci. Eng 18(2), 021006 (Mar 16, 2018) (10 pages) Paper No: JCISE-16-2113; doi: 10.1115/1.4039430 History: Received October 24, 2016; Revised February 07, 2018

For vision-based measurement, there are few research or professional tools for local contour positional errors of flexible automotive rubber strips. To support the automatic measurement of contour positional errors, a novel local contour registration and measurement method based on shape descriptors is proposed. In this method, a shape descriptor is proposed to find correspondence between a reference local contour and a desired local contour. First, a shape descriptor that includes the shape representation and restrictions of the local contour is extracted from the reference contour. Second, several tolerable shape descriptors for a desired actual local contour are constructed by adding some loosening factors to the ideal descriptor, and an angular similarity-based searching strategy is used to find the best actual local contour. Finally, from the matched local point sets, a quantitative calculation step provides the desired deviation values. This method is implemented in a sealing strip cross section measurement system, and numerous cross-sectional profiles are tested. The experimental results verify the stability and effectiveness of the proposed method. Important progress toward the automatic measurement of flexible products is demonstrated.

Copyright © 2018 by ASME
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Angrisani, L. , Daponte, P. , Pietrosanto, A. , and Liguori, C. , 1999, “An Image-Based Measurement System for the Characterisation of Automotive Gaskets,” Measurement, 25(3), pp. 169–181. [CrossRef]
Liguori, C. , Paolillo, A. , and Pietrosanto, A. , 2004, “An On-Line Stereo-Vision System for Dimensional Measurements of Rubber Extrusions,” Measurement, 35(3), pp. 221–231. [CrossRef]
Anchini, R. , Di Leo, G. , Liguori, C. , and Paolillo, A. , 2009, “Metrological Characterization of a Vision-Based Measurement System for the Online Inspection of Automotive Rubber Profile,” IEEE Trans. Instrum. Meas., 58(1), pp. 4–13. [CrossRef]
Karunasena, C. , and Wickramarachchi, N. , 2010, “Vision Based Cross Sectional Area Estimator for Industrial Rubber Profile Extrusion Process Controlling,” Fifth International Conference on Information and Automation for Sustainability (ICIAFs), Colombo, Sri Lanka, Dec. 17–19, pp. 144–149.
Bevilacqua, M. , Di Leo, G. , Landi, M. , and Paolillo, A. , 2013, “Multi-Exposure Imaging for Measurements in Rubber Production,” Meas. Sci. Technol., 24(7), pp. 74014–74022. [CrossRef]
Santo, M. D. , Liguori, C. , Paolillo, A. , and Pietrosanto, A. , 2004, “Standard Uncertainty Evaluation in Image-Based Measurements,” Measurement, 36(3–4), pp. 347–358. [CrossRef]
Shirmohammadi, S. , and Ferrero, A. , 2014, “Camera as the Instrument: The Rising Trend of Vision Based Measurement,” IEEE Instrum. Meas. Mag., 17(3), pp. 41–47. [CrossRef]
Zhu, Y. , Tu, Y. X. , and Du, Z. C. , 2012, “A Block-Registration Algorithm for Strip of Section Image Based on Corner Points Matching,” Mach. Des. Res., 28(3), pp. 55–57 (in Chinese).
Li, J. , Du, Z. , Zhang, Y. , and Wang, Y. , 2015, “Geometric Descriptor-Based Local Contour Point Set Matching for Cross-Section Profile Measurement of Automotive Sealing Strips,” ASME Paper No. DETC2015-47127 .
Zitová, B. , and Flusser, J. , 2003, “Image Registration Methods: A Survey,” Image Vision Comput., 21(11), pp. 977–1000. [CrossRef]
Debayle, J. , and Presles, B. , 2016, “Rigid Image Registration by General Adaptive Neighborhood Matching,” Pattern Recognit., 55, pp. 45–57. [CrossRef]
Li, M. , Wittek, A. , and Miller, K. , 2014, “Efficient Inverse Isoparametric Mapping Algorithm for Whole-Body Computed Tomography Registration Using Deformations Predicted by Nonlinear Finite Element Modeling,” ASME J. Biomech. Eng., 136(8), p. 084503. [CrossRef]
Sorokin, D. V. , Peterlik, I. , Tektonidis, M. , Rohr, K. , and Matula, P. , 2018, “Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model,” IEEE Trans. Med. Imaging, 37(1), pp. 173–184. [CrossRef] [PubMed]
Dezhi, W. , Yu, J. , Weizhuo, W. , and Yueqi, W. , 2016, “Bias Reduction in Sub-Pixel Image Registration Based on the Anti-Symmetric Feature,” Meas. Sci. Technol., 27(3), p. 035206. [CrossRef]
Zhang, Z. , 1994, “Iterative Point Matching for Registration of Free-Form Curves and Surfaces,” Int. J. Comput. Vision, 13(2), pp. 119–152. [CrossRef]
Kwok, T.-H. , and Tang, K. , 2015, “Improvements to the Iterative Closest Point Algorithm for Shape Registration in Manufacturing,” ASME J. Manuf. Sci. Eng., 138(1), p. 011014. [CrossRef]
Fischler, M. A. , and Bolles, R. C. , 1981, “Random Sample Consensus: A Paradigm for Model Fitting With Applications to Image Analysis and Automated Cartography,” Commun. ACM, 24(6), pp. 381–395. [CrossRef]
Ma, J. , Zhao, J. , Jiang, J. , and Zhou, H. , 2017, “Non-Rigid Point Set Registration With Robust Transformation Estimation Under Manifold Regularization,” Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, Feb. 4–9, pp. 4218–4224. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14188/14303
Huang, J. , Yuan, Y. , Wang, Z. , Qi, Z. , Xing, C. , and Gao, J. , 2018, “A Global-to-Local Registration and Error Evaluation Method of Blade Profile Lines Based on Parameter Priority,” Int. J. Adv. Manuf. Technol., 94(9–12), pp. 3829–3839.
Maddala, K. T. , Moss, R. H. , Stoecker, W. V. , Hagerty, J. R. , Cole, J. G. , Mishra, N. K. , and Stanley, R. J. , 2017, “Adaptable Ring for Vision-Based Measurements and Shape Analysis,” IEEE Trans. Instrum. Meas., 66(4), pp. 746–756.
Di Leo, G. , Liguori, C. , Paolillo, A. , and Pietrosanto, A. , 2015, “Machine Vision Systems for On Line Quality Monitoring in Industrial Applications,” Acta IMEKO, 4(1), pp. 121–127. [CrossRef]
Anchini, R. , Di Leo, G. , Liguori, C. , and Paolillo, A. , 2006, “New Measurement Techniques for the On Line Dimension Characterization of Automotive Rubber Profiles,” XVIII IMEKO World Congress: Metrology for a Sustainable Development, Rio de Janeiro, Brazil, Sept. 17–22.


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Fig. 3

A real captured image of the cross section of a sealing strip

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Fig. 2

Scheme of descriptor-based local contour registration and measurement algorithm

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Fig. 4

An example of a reduced shape of a point set

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Fig. 5

An example of a direction vector of a reduced shape of a point set

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Fig. 6

Examples of four local point sets

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Fig. 7

Examples of reduced shapes and direction vectors of four local point sets

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Fig. 8

An example of the contour length and the fluctuation of a point set

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Fig. 1

Reference drawing and definition of an angular positional tolerance

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Fig. 9

Examples of a standard local contour and a lower tolerance limited line

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Fig. 10

Examples of quantification of angular positional tolerance

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Fig. 12

The hardware of the sealing strip section system

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Fig. 11

Examples of the measurement of positional tolerance

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Fig. 13

Registration algorithm accuracy test: (a) dimensions of template (mm) and (b) registration results for template

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Fig. 14

A matched image and its reference after global matching

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Fig. 15

Three matched point sets (view of local part)

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Fig. 16

Qualification of positional tolerances: (a) definition module in the measurement software and (b) illustration of tolerance



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