Ocean Systems Engineering

  • 제6권3호
  • /
  • Pages.217-231
  • /
  • 2016
  • /
  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An adaptive method of multi-scale edge detection for underwater image

  • Bo, Liu (Dalian Scientific Test and Control Technology Research Institute)
  • 투고 : 2016.06.01
  • 심사 : 2016.08.30
  • 발행 : 2016.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper presents a new approach for underwater image analysis using the bi-dimensional empirical mode decomposition (BEMD) technique and the phase congruency information. The BEMD algorithm, fully unsupervised, it is mainly applied to texture extraction and image filtering, which are widely recognized as a difficult and challenging machine vision problem. The phase information is the very stability feature of image. Recent developments in analysis methods on the phase congruency information have received large attention by the image researchers. In this paper, the proposed method is called the EP model that inherits the advantages of the first two algorithms, so this model is suitable for processing underwater image. Moreover, the receiver operating characteristic (ROC) curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using the EP model. The EP images are computed using combinations of the Canny detector parameters, and the binaryzation image results are generated accordingly. The ideal EP edge feature extractive maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.

키워드

과제정보

연구 과제 주관 기관 : Liao Ning Province Education Commission

참고문헌

  1. Bhuiyan, S.M.A, Adhami, R.R., Ranganath, H.S. et al. (2008c), "Aurora image denoising with a modified bi-dimensional empirical mode decomposition method", Proceedings of the IEEE Southeast Con 2008, Alabama: Institute of Electrical and Electronics Engineers, Huntsville, USA.
  2. Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008), "Edge detection via a fast and adaptive bidimensional empirical mode decomposition", Proceedings of the Machine Learning for Signal Processing, Cancun, Mexico: IEEE Signal Processing Society.
  3. Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008), "Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation", J. EURASIP Journal on Advances in Signal Processing, 2008, 1-18.
  4. Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008a), "Fast and adaptive bi-dimensional empirical mode decomposition using order-statistics filter based envelope estimation", EURASIP J. Adv. Signal Pr., 1-18.
  5. Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008b), "Edge detection via a fast and adaptive bi-dimensional empirical mode decomposition", Proceedings of the Machine Learning for Signal Processing, Cancun, Mexico.
  6. Bhuiyan, S.M.A., Adhami, R.R., Ranganath, H.S. et al. (2008), "Aurora image denoising with a modified bidimensional empirical mode decomposition method", Proceedings of the IEEE Southeast Con 2008, Huntsville, Alabama: Institute of Electrical and Electronics Engineers.
  7. Blair, D.G. (2006), "Underwater acoustic imaging: image due to a specular reflector in the geometrical-acoustics limit", J. Marine Sci. Technol., 11(2), 123-130. https://doi.org/10.1007/s00773-005-0208-z
  8. Blair, D.G. (2006), "Underwater acoustic imaging: image due to a specular reflector in the geometrical acoustics limit", J. Marine Sci. Technol., 11(2), 123-130. https://doi.org/10.1007/s00773-005-0208-z
  9. Boyle, F. (2003), "Image processing techniques for underwater acoustic image enhancement", J. Acoust. Soc. Am., 114(4), 2398-2399.
  10. Boyle, F. (2003), "Image processing techniques for underwater acoustic image enhancement", J. Acoust. Soc. Am., 114(4), 2398-2399.
  11. Chen, H.H. (2002), "Variation reduction in quality of an optical triangulation system employed for underwater range finding", J. Ocean Eng., 29(15), 1871-1893. https://doi.org/10.1016/S0029-8018(02)00004-5
  12. Chen, H.H. (2002), "Variation reduction in quality of an optical triangulation system employed for underwater range finding", Ocean Eng., 29(15), 1871-1893. https://doi.org/10.1016/S0029-8018(02)00004-5
  13. Chen, H.H. and Wu, C.M. (2004), "An algorithm of image processing for underwater range finding by active triangulation", J. Ocean Eng., 31(8-9), 1037-1062. https://doi.org/10.1016/j.oceaneng.2003.10.007
  14. Chen, H.H. and Wu, C.M. (2004), "An algorithm of image processing for underwater range finding by active triangulation", Ocean Eng., 31(8-9), 1037-1062. https://doi.org/10.1016/j.oceaneng.2003.10.007
  15. Damerval, C., Meignen, S. and Perrier, V. (2005), "A fast algorithm for bidimensional EMD", J. IEEE Signal Processing Letters, 12(10), 701-704. https://doi.org/10.1109/LSP.2005.855548
  16. Damerval, C., Meignen, S. and Perrier, V. (2005), "A fast algorithm for bi-dimensional EMD", J. IEEE Signal Processing Letters, 12(10), 701-704. https://doi.org/10.1109/LSP.2005.855548
  17. Ge, G.T., Sang, E.F., Liu, Z.F. et al. (2007), "Underwater acoustic feature extraction based on bi-dimensional empirical mode decomposition in shadow field", Proceedings of the signal design and its applications in communications, Southwest Jiaotong University, Chengdu, China.
  18. Guangtao, G.E., Enfang, S., Zhuofu, L. et al. (2007), "Underwater acoustic feature extraction based on bidimensional empirical mode decomposition in shadow field", Proceedings of the signal design and its applications in communications, Chengdu: Southwest Jiaotong University.
  19. Huang, N.E., Shen, Z., Long, S.R. et al. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", J. Proceedings of the Royal Society of London: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. https://doi.org/10.1098/rspa.1998.0193
  20. Huang, N.E., Shen, Z., Long, S.R. et al. (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society of London: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. https://doi.org/10.1098/rspa.1998.0193
  21. Jaffe, J.S. (1998), "Underwater optical imaging: the design of optimal systems", J. Oceanography, 11(1), 40-41.
  22. Jaffe, J.S. (1998), "Underwater optical imaging: the design of optimal systems", Oceanography, 11(1), 40-41.
  23. Jaffe, J.S. (2001), "Underwater optical imaging: status and prospects", J. Oceanography, 14(3), 64-75. https://doi.org/10.5670/oceanog.2001.24
  24. Jaffe, J.S. (2001), "Underwater optical imaging: status and prospects", Oceanography, 14(3), 64-75. https://doi.org/10.5670/oceanog.2001.24
  25. Kovesi, P. (1999), "Image features from phase congruency", J. Videre: J. Comput. Vision Res., 1(3), 1-27.
  26. Kovesi, P. (1999), "Image Features from Phase Congruency", Videre: J. Comput. Vision Res., 1(3), 1-27.
  27. Kovesi, P. (2002), "Edges are not just steps", Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia.
  28. Kovesi, P. (2002), "Edges are not just steps", Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia.
  29. Liu, B. and Lin, Y. (2012), "A method for underwater image analysis using bi-dimensional empirical mode decomposition technique", Ocean Syst. Eng., 2(2), 137-145. https://doi.org/10.12989/ose.2012.2.2.137
  30. Liu, B. and Lin, Y. (2012), "A method for underwater image analysis using bi-dimensional empirical mode decomposition technique", Ocean Syst. Eng., 2(2), 137-145. https://doi.org/10.12989/ose.2012.2.2.137
  31. Macmillan, N.A. and Creelman, C.D. (1991), Detection Theory: A User's Guide, Cambridge: Cambridge Univ. Press.
  32. Macmillan, N.A. and Creelman, C.D. (1991), Detection Theory: A User's Guide, Cambridge Univ. Press, Cambridge, U. K.
  33. Nevis, A. (1999), "Adaptive background equalization and image processing applications for laser line scan data", J. Proceedings of SPIE, 3710(2),1260-1271.
  34. Nevis, A. (1999), "Adaptive background equalization and image processing applications for laser line scan data", Proceedings of SPIE, 3710(2), 1260-1271.
  35. Nunes, J.C. and Bouaoune, Y. and Delechelle, E. et al. (2003), "Image analysis by bidimensional empirical mode decomposition", J. Image Vision Comput., 21(12), 1019-1026. https://doi.org/10.1016/S0262-8856(03)00094-5
  36. Nunes, J.C., Bouaoune, Y., Delechelle, E. et al. (2003), "Image analysis by bi-dimensional empirical mode decomposition", Image and Vision Computing, 21(12), 1019-1026. https://doi.org/10.1016/S0262-8856(03)00094-5
  37. Nunes, J.C., Guyot, S. and Delechelle, E. (2005), "Texture analysis based on local analysis of the bidimensional empirical mode decomposition", J. Machine Vision Appl., 16(3), 177-188. https://doi.org/10.1007/s00138-004-0170-5
  38. Nunes, J.C., Guyot, S. and Deléchelle, E. (2005), "Texture analysis based on local analysis of the bi-dimensional empirical mode decomposition", Machine Vision and Appl., 16(3), 177-188. https://doi.org/10.1007/s00138-004-0170-5
  39. Nunes, J.C., Niang, O., Bouaoune, Y. et al. (2003), "Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models", J. IEEE Machine Vision Appl., 2, 633-635.
  40. Nunes, J.C., Niang, O., Bouaoune, Y. et al. (2003), "Texture analysis based on the bi-dimensional empirical mode decomposition with gray-level co-occurrence models", IEEE Machine Vision and Application, 2, 633-635.
  41. Rilling, G., Flandrin, P., Goncalves, P. et al. (2007), "Bivariate empirical mode decomposition", J. IEEE Signal Processing Lett., 14(12), 936-939. https://doi.org/10.1109/LSP.2007.904710
  42. Rilling, G., Flandrin, P., Goncalves, P. et al. (2007), "Bivariate empirical mode decomposition", J. IEEE Signal Processing Letters, 14(12), 936-939. https://doi.org/10.1109/LSP.2007.904710
  43. Theo, P. (1990), "Integrating region growing and edge detection", J. IEEE Trans. On Pattern Analysis and Machine Intelligence, 12(3), 225-233. https://doi.org/10.1109/34.49050
  44. Theo. P. (1990), "Integrating region growing and edge detection", IEEE Trans. On Pattern Analysis and Machine Intelligence, 12(3), 225-233. https://doi.org/10.1109/34.49050
  45. Xu Y., Liu, B., Liu, J. et al. (2006), "Two-dimensional empirical mode decomposition by finite elements", J. Proceedings of the Royal Society A, 462(2074), 3081-3096. https://doi.org/10.1098/rspa.2006.1700
  46. Xu, X., Li, H. and Wang, A.N. (2007), "The application of BEMD to multi-spectral image fusion", Proceedings of the Wavelet Analysis and Pattern Recognition (ICWAPR), Beijing: University of Science & Technology.
  47. Xu, X., Li, H. and Wang, A.N. (2007), "The application of BEMD to multi-spectral image fusion", Proceedings of the Wavelet Analysis and Pattern Recognition (ICWAPR), Beijing University of Science & Technology, Beijing, China.
  48. Xu, Y., Liu, B., Liu, J. et al. (2006), "Two-dimensional empirical mode decomposition by finite elements", Proceedings of the Royal Society A, 462(2074), 3081-3096. https://doi.org/10.1098/rspa.2006.1700
  49. Yitzhaky, Y. and Peli, E. (2003), "A method for objective edge detection evaluation and detector parameter selection", J. IEEE Transactions on Image Processing, 25(8), 1027-1033.
  50. Yitzhaky, Y. and Peli, E. (2003), "A method for objective edge detection evaluation and detector parameter selection", IEEE Transactions on Image Processing, 25(8), 1027-1033.