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Optical Society of Korea (한국광학회)
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Nonlinear Diffusion and Structure Tensor Based Segmentation of Valid Measurement Region from Interference Fringe Patterns on Gear Systems

  • Wang, Xian (State Key Laboratory of Manufacturing Systems Engineering, Xi'an Jiaotong University) ;
  • Fang, Suping (State Key Laboratory of Manufacturing Systems Engineering, Xi'an Jiaotong University) ;
  • Zhu, Xindong (State Key Laboratory of Manufacturing Systems Engineering, Xi'an Jiaotong University) ;
  • Ji, Jing (State Key Laboratory of Manufacturing Systems Engineering, Xi'an Jiaotong University) ;
  • Yang, Pengcheng (College of Mechanical & Electrical Engineering, Xi'an Polytechnic University) ;
  • Komori, Masaharu (Department of Precision Engineering, Kyoto University) ;
  • Kubo, Aizoh (Department of Precision Engineering, Kyoto University)
  • Received : 2016.11.21
  • Accepted : 2017.10.17
  • Published : 2017.12.25
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Abstract

The extraction of the valid measurement region from the interference fringe pattern is a significant step when measuring gear tooth flank form deviation with grazing incidence interferometry, which will affect the measurement accuracy. In order to overcome the drawback of the conventionally used method in which the object image pattern must be captured, an improved segmentation approach is proposed in this paper. The interference fringe patterns feature, which is smoothed by the nonlinear diffusion, would be extracted by the structure tensor first. And then they are incorporated into the vector-valued Chan-Vese model to extract the valid measurement region. This method is verified in a variety of interference fringe patterns, and the segmentation results show its feasibility and accuracy.

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References

  1. S. Fang, L. Wang, P. Yang, L. Meng, M. Komori, and A. Kubo, "Improvement of the oblique-incidence optical interferometric system to measure tooth flanks of involute helical gears," Opt. Soc. Am. 28, 590-595 (2011). https://doi.org/10.1364/JOSAA.28.000590
  2. S. Fang, L. Wang, S. Liu, M. M. Komori, and A. Kubo, "Positioning the actual interference fringe pattern on the tooth flank in measuring gear tooth flanks by laser interferometry," Opt. Eng. 50, 055601 (2011). https://doi.org/10.1117/1.3578401
  3. S.-P. Fang, L.-J. Wang, M. Komori, A. Kubo, "Design of laser interferometric system for measurement of gear tooth flank," Optik - Int. J. Light Electron Opt. 122, 1301-1304 (2011). https://doi.org/10.1016/j.ijleo.2010.09.002
  4. H. A. Vrooman and A. A. Maas, "Image processing algorithms for the analysis of phase-shifted speckle interference patterns," Appl. Opt. 30, 1636-1641 (1991). https://doi.org/10.1364/AO.30.001636
  5. T. Ino and T. Yatagai, "Oblique incidence interferometry for gear-tooth surface profiling," Proc. SPIE 1720, pp. 464-469.
  6. S. Fang, L. Wang, P. Yang, L. Meng, and M. Komori, "Object-image-based method to construct an unweighted quality map for phase extraction and phase unwrapping," Appl. Opt. 50, 1482-1487 (2011). https://doi.org/10.1364/AO.50.001482
  7. V. Estellers, D. Zosso, R. Lai, S. Osher, J. Thiran, and X. Bresson, "An efficient algorithm for level set method preserving distance function," IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 21, 4722-4734 (2012).
  8. J. Zhang, G. Wang, Z. Pan, and B. Huang, "Unsupervised color texture segmentation using active contour model and oscillating information," in Ninth International Conference on Machine Vision, (International Society for Optics and Photonics, 2017), pp. 1034116.
  9. A. Tatu and S. Bansal, "A novel active contour model for texture segmentation," in International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, (Springer International Publishing, 2015), pp. 223-236.
  10. K. Sarafrazi, M. Yazdi, and M. J. Abedini, "A new image texture segmentation based on contourlet fractal features," Arabian J. Sci. Eng. 38, 3437-3449 (2013). https://doi.org/10.1007/s13369-013-0624-z
  11. K. O. Na and C. Kim, "Unsupervised texture segmentation of natural scene images using region-based markov random field," J. Signal Process. Syst. 83, 423-436 (2016). https://doi.org/10.1007/s11265-015-1030-4
  12. T. Boonnuk, S. Srisuk, and T. Sripramong, "Texture segmentation using active contour model with edge flow vector," Int. J. Inf. Electron. Eng. 5, 107 (2015).
  13. N. Paragios and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," Int. J. Computer Vision 46, 223-247 (2002). https://doi.org/10.1023/A:1014080923068
  14. C. Sagiv, N. A. Sochen, and Y. Y. Zeevi, "Texture segmentation via a diffusion-segmentation scheme in the gabor feature space," in Proc. Texture 2002, 2nd International Workshop on Texture Analysis and Synthesis, 2002), pp. 123-128.
  15. B. Sandberg, T. Chan, and L. Vese, "A level-set and Gaborbased active contour algorithm for segmenting textured images," in UCLA Department of Mathematics CAM Report, (Citeseer, 2002), pp. 02-39.
  16. K. Betty and P. Sasi Kiran, "Optimal gabor filter for multi scale texture segmentation with color indication," IJRCCT 4, 708-713 (2015).
  17. M. Rousson, T. Brox, and R. Deriche, "Active unsupervised texture segmentation on a diffusion based feature space," in Proc. Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. II-699-704.
  18. T. Brox and J. Weickert, "Nonlinear matrix diffusion for optic flow estimation," in Pattern Recognition (Springer, 2002), pp. 446-453.
  19. R. M. Patil and A. A. Manjrekar, "A novel image classification and image retrieval using saliency driven multiscale nonlinear diffusion filtering and linear distance coding," in International Conference on Information Processing, 2016), pp. 338-343.
  20. N. H. Ha and P. T. Nhu, "Noise filtering and detecting edge image in nonlinear anisotropic diffusion," Vietnam J. Sci. Technol. 48, 1-9 (2010).
  21. C. Tang, L. Han, H. Ren, D. Zhou, Y. Chang, X. Wang, and X. Cui, "Second-order oriented partial-differential equations for denoising in electronic-speckle-pattern interferometry fringes," Opt. Lett. 33, 2179-2181 (2008). https://doi.org/10.1364/OL.33.002179
  22. J. Weickert, "Anisotropic diffusion in image processing," B.g.teubner Stuttgart 16, 272 (1996).
  23. T. Brox, J. Weickert, B. Burgeth, and P. Mrazek, "Nonlinear structure tensors," Image Vision Comput. 24, 41-55 (2006). https://doi.org/10.1016/j.imavis.2005.09.010
  24. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. Mach. Intell. 12, 629-639 (1990). https://doi.org/10.1109/34.56205
  25. L. Alvarez, P.-L. Lions, and J.-M. Morel, "Image selective smoothing and edge detection by nonlinear diffusion. II," SIAM J. Numer. Anal. 29, 845-866 (1992). https://doi.org/10.1137/0729052
  26. G. Gerig, O. Kübler, R. Kikinis, and F. A. Jolesz, "Nonlinear anisotropic filtering of MRI data," IEEE Trans. Med. Imag. 11, 221-232 (1992). https://doi.org/10.1109/42.141646
  27. X. Yang, Q. Yu, and S. Fu, "An algorithm for estimating both fringe orientation and fringe density," Opt. Commun. 274, 286-292 (2007). https://doi.org/10.1016/j.optcom.2007.02.020
  28. T. F. Chan and L. Vese, "Active contours without edges," IEEE Trans. Image Process. 10, 266-277 (2001). https://doi.org/10.1109/83.902291
  29. T. F. Chan, B. Y. Sandberg, and L. A. Vese, "Active contours without edges for vector-valued images," J. Visual Commun. Image Representation 11, 130-141 (2000). https://doi.org/10.1006/jvci.1999.0442
  30. M. Meila, "Comparing clusterings-an information based distance," Journal of Multivariate Analysis 98, 873-895 (2007). https://doi.org/10.1016/j.jmva.2006.11.013
  31. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, (IEEE, 2001), pp. 416-423.
  32. C. Pantofaru and M. Hebert, "A comparison of image segmentation algorithms," in Biomedical Engineering International Conference, 2015), pp. 1-4.
  33. X. Wang, S. Fang, and X. Zhu, "Weighted least-squares phase unwrapping algorithm based on a non-interfering image of an object," Appl. Opt. 56, 4543 (2017). https://doi.org/10.1364/AO.56.004543