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An improved industrial sub-pixel edge detection algorithm based on coarse and precise location

  • Xin XieEmail author
  • Songlin Ge
  • Mingye Xie
  • Fengping Hu
  • Nan Jiang
Original Research
  • 4 Downloads

Abstract

In this paper, an improved sub-pixel edge detection algorithm combining coarse and precise location is proposed. The algorithm fully considers the 8-neighborhood pixel information and keeps the Roberts operator’s advantages of high location accuracy and fast speed. Meanwhile, it can effectively suppress noise and obtain better detection results. In order to solve the problem of low efficiency of the Zernike moment method in threshold selection, the Otsu’s method is introduced to achieve accurate sub-pixel edge location. The experimental results show that the proposed algorithm effectively improves the detection efficiency and the detection accuracy.

Keywords

Edge detection Sub-pixel Roberts operator Zernike moment Otsu’s method 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China, under Grant Nos. 61762037, 61872141, 61462028, Natural Science Foundation of Jiangxi Province, under Grant No. 20181BAB206037, Excellent Scientific and Technological Innovation Teams of Jiangxi Province, under Grant No. 20181BCB24009 and Nanchang City Knowledge Innovation Team, under Grant No. 2016T75.

References

  1. Che JK, Ratnam MM (2018) Real-time monitoring of workpiece diameter during turning by vision method. Measurement 126:369–377.  https://doi.org/10.1016/j.measurement.2018.05.089 CrossRefGoogle Scholar
  2. Chen QC, Hou YQ, Tan QC (2016) A subpixel edge detection method based on an arctangent edge model. Opt Int J Light Electron Opt 127(14):5702–5710CrossRefGoogle Scholar
  3. Connolly C (2009) Machine vision advances and applications. Assembly Autom 29(2):106–111CrossRefGoogle Scholar
  4. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306MathSciNetCrossRefzbMATHGoogle Scholar
  5. Duan R, Li Q, Li YY (2005) Summary of image edge detection. Opt Tech 3(3):415–419Google Scholar
  6. Duan ZY, Wang N, Fu JS, Zhao WH, Duan BQ, Zhao JG (2018) High precision edge detection algorithm for mechanical parts. Meas Sci Rev 18(2):65–71CrossRefGoogle Scholar
  7. Georgescu Carmina (2018) Improved edge detection algorithms based on a riesz fractional derivative. In: International conference image analysis and recognition, Springer, pp 201–209Google Scholar
  8. Gester D, Simon S (2018) A spatial moments sub-pixel edge detector with edge blur compensation for imaging metrology. In: 2018 IEEE international instrumentation and measurement technology conference (I2MTC), pp 1–6Google Scholar
  9. Ghosal S, Mehrotra R (1993) Orthogonal moment operators for subpixel edge detection. Pattern Recognit 26(2):295–306CrossRefGoogle Scholar
  10. Gong YX, Li XC, Zhang H, Liu QJ, Sun YT (2017) An improved canny algorithm based on adaptive 2d-otsu and newton iterative. In: IEE 2nd international conference on image, vision and computing (ICIVC), 2017, pp 67–71Google Scholar
  11. Gonzalez CI, Melin P, Castillo O (2017) Edge detection method based on general type-2 fuzzy logic applied to color images. Information 8(3):104CrossRefGoogle Scholar
  12. Gonzalez CI, Melin P, Castro JR, Castillo O, Mendoza O (2016a) Optimization of interval type-2 fuzzy systems for image edge detection. Appl Soft Comput 47:631–643CrossRefGoogle Scholar
  13. Gonzalez CI, Melin P, Castro JR, Mendoza O, Castillo O (2016b) An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft Comput 20(2):773–784CrossRefGoogle Scholar
  14. Guo LY, Li SN, Hu WJ, Wu JH, Tu B, He W, Ou XF, Zhang GY (2018) Sub-pixel level defect detection based on notch filter and image registration. Int J Pattern Recognit Artif Intell 32(06):1854016MathSciNetCrossRefGoogle Scholar
  15. He YB, Zeng YJ, Chen HX, Xiao SX, Wang YW, Huang SY (2018) Research on improved edge extraction algorithm of rectangular piece. Int J Modern Phys C 29(01):1850007CrossRefGoogle Scholar
  16. Hueckel MH (1971) An operator which locates edges in digitized pictures. J ACM 18(1):113–125CrossRefzbMATHGoogle Scholar
  17. Jiang M, Ma N (2015) Sub-pixel edge detection method based on zernike moment. In: IEEE 27th Chinese control and decision conference (CCDC), 2015, pp 3673–3676Google Scholar
  18. Li X, Zhang H (2017) An improved canny edge detection algorithm. In: 2017 8th IEEE international conference on software engineering and service science (ICSESS), pp  275–278Google Scholar
  19. Liu WT, Chen Z, Zhang XM (2014) An improved method for subpixel edge detection using gray moment. J Test Meas Technol 6:010Google Scholar
  20. Liu ZX, Liu JY, Liu, Li Y, Weng LM (2018) A fast tool edge detection method based on zernike moments algorithm. IOP conference series: materials science and engineering. IOP Publishing, Bristol, p p 032106Google Scholar
  21. Luo M, Wang Y (2011) Subpixel edge measurement method based on roberts-zernike moments operator. Jisuanji Gongcheng yu Yingyong (Comput Eng Appl) 47(5):169–171Google Scholar
  22. Lyvers EP, Robert Mitchell O, Akey ML, Reeves AP (1989) Subpixel measurements using a moment-based edge operator. IEEE Trans Pattern Anal Mach Intell 11(12):1293–1309CrossRefGoogle Scholar
  23. Melin P, Gonzalez CI, Castro JR, Mendoza O, Castillo O (2014) Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans Fuzzy Syst 22(6):1515–1525CrossRefGoogle Scholar
  24. Nguyen KWL, Aprilia A, Khairyanto A, Pang WC, Seet GGL, Tor SB (2018) Edge detection from point cloud of worn parts. In: Proceedings of the 3rd international conference on progress in additive manufacturing (Pro-AM 2018), pp 595–600.  https://doi.org/10.25341/D45C7S
  25. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  26. Peng SH, Su WQ, Hu X, Liu CH, Wu Y, Nam H (2018) Subpixel edge detection based on edge gradient directional interpolation and zernike moment. In: DEStech transactions on computer science and engineering, (csse).  https://doi.org/10.12783/dtcse/csse2018/24488
  27. Prasad R, Suresh S (2016) A review on edge detection algorithms. IJMCA 4(1):007–011Google Scholar
  28. Roberts LG (1963) Machine perception of three-dimensional solids. PhD thesis, Massachusetts Institute of TechnologyGoogle Scholar
  29. Kumar Singh R, Shekhar S, Bhawan Singh R, Chauhan V (2014) A comparative study of edge detection techniques. Int J Comput Appl 100(19):5–8.  https://doi.org/10.5120/17631-5949 Google Scholar
  30. Tabatabai AJ, Robert Mitchell O (1984) Edge location to subpixel values in digital imagery. IEEE Trans Pattern Anal Mach Intell 2:188–201CrossRefGoogle Scholar
  31. Tian GF, Gao F (2015) Research of an improved algorithm about sub-pixel edge detection for images. Microcomput Appl 21:014Google Scholar
  32. Vijaya Kumar Reddy R, Prudvi Raju K, Jogendra Kumar M, Ravi Kumar L, Ravi Prakash P, Sai Kumar S (2017) Comparative analysis of common edge detection algorithms using pre-processing technique. Int J Electr Comput Eng 7(5):2574–2580Google Scholar
  33. Wang ZW (2012) Comparison research of capability of several detection operators for edge detection. Manuf Autom 11:006Google Scholar
  34. Wang CF (2016) Improved sub-pixel edge location based on spatial moment. Int J Simulc Syst Sci Technol 17:3Google Scholar
  35. Wei BZ, Zhao ZM (2013) A sub-pixel edge detection algorithm based on zernike moments. Imaging Sci J 61(5):436–446CrossRefGoogle Scholar
  36. Wen YG, He HZ, Li HY (2014) An improved image edge detection algorithm based on roberts and grey relational analysis. J Graphics 4:025Google Scholar
  37. Yu XL, Lin X, Dai YQ, Zhu KP (2017) Image edge detection based tool condition monitoring with morphological component analysis. ISA Trans 69:315–322CrossRefGoogle Scholar
  38. Yu WB, Ma YH, Wu X, Liu KP (2015) Research of improved subpixel edge detection algorithm using zernike moments. In: IEEE Chinese Automation Congress (CAC), 2015, pp 712–716Google Scholar
  39. Zhou ZH, Shuliang YE, Zhu WB (2017) A feature image-based method for evaluating small modulus gear sub-pixel edge-detection effectiveness. J China Univ Metrol 28(1):29–34Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xin Xie
    • 1
    Email author
  • Songlin Ge
    • 1
  • Mingye Xie
    • 2
  • Fengping Hu
    • 3
  • Nan Jiang
    • 1
  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangPeople’s Republic of China
  2. 2.School of Information Science TechnologyEast China Normal UniversityShanghaiPeople’s Republic of China
  3. 3.School of Civil EngineeringEast China Jiaotong UniversityNanchangPeople’s Republic of China

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