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http://dx.doi.org/10.12989/ose.2012.2.2.137

A method for underwater image analysis using bi-dimensional empirical mode decomposition technique  

Liu, Bo (State Key Laboratory of Structural Analysis for Industrial Equipment, School of Naval Architecture and Ocean Engineering, Dalian University of Technology)
Lin, Yan (State Key Laboratory of Structural Analysis for Industrial Equipment, School of Naval Architecture and Ocean Engineering, Dalian University of Technology)
Publication Information
Ocean Systems Engineering / v.2, no.2, 2012 , pp. 137-145 More about this Journal
Abstract
Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.
Keywords
underwater image; bi-dimensional empirical mode decomposition; edge feature detector; phase congruency; multiple pixel edge extraction;
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1 Rilling, G., Flandrin, P., Goncalves, P. and Lilly, J.M. (2007), "Bivariate empirical mode decomposition", Signal Proc. Lett., 14(12), 936-939.   DOI
2 Xu, X., LI, H. and Wang, A.N. (2007), "The application of BEMD to multi-spectral image fusion", Wavelet Analysis and Pattern Recognition. ICWAPR 2007. International Conference.
3 Xu, Y., Liu, B., Liu, J. and Riemenschneider, S. (2006), "Two-dimensional empirical mode decomposition by finite elements", Proceedings of the Royal Society A.
4 Bhuiyan, S.M.A., Adhami, R.R., Ranganath, H.S. and Khan, J.F. (2008c), "Aurora image denoising with a modified bidimensional empirical mode decomposition method", IEEE.
5 Boyle, F. (2003), "Image processing techniques for underwater acoustic image enhancement", J. Acoust. Soc. Am., 114 (4), 2398-2399.
6 Chen, H.H. (2002), "Variation reduction in quality of an optical triangulation system employed for underwater range finding", Ocean Eng., 29(15), 1871-1893.   DOI   ScienceOn
7 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.   DOI   ScienceOn
8 Blair, D.G. (2006), "Underwater acoustic imaging: image due to a specular reflector in the geometrical-acoustics limit", J. Mar. Sci. Technol., 11(2), 123-130.   DOI   ScienceOn
9 Damerval, C., Meignen, S. and Perrier, V. (2005), "A fast algorithm for bidimensional EMD", IEEE Signal Proc. Lett.,12(10),701-704.   DOI
10 Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C. and Liu, H. H., (1998), "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proceedings of the Royal Society of London. Series A: Mathematical, Phys. Eng. Sci., 454(1971), 903-995.   DOI   ScienceOn
11 Guangtao, G., Enfang, S., Zhuofu, L. and BeiBei, Z. (2007), "Underwater acoustic feature extraction based on bidimensional empirical mode decomposition in shadow field", Proceedings of the Signal Design and Its Applications in Communications. IWSDA 2007. 3rd International Workshop.
12 Nunes, J.C., Niang, O. Bouaoune, Y., Delechelle, E. and Bunel, Ph. (2003a), "Texture analysis based on the bidimensional empirical mode decomposition with gray-level co-occurrence models", Proceedings of the Signal Processing and Its Applications, 7th International Symposium.
13 Nunes, J.C., Guyot, S. and Delechelle, E. (2005), "Texture analysis based on local analysis of the bidimensional empirical mode decomposition", Mach. Vision Appl., 16(3), 177-188.   DOI   ScienceOn
14 Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O. and Bunel, Ph. (2003b), "Image analysis by bidimensional empirical mode decomposition", Image Vision Comput., 21(12), 1019-1026.   DOI   ScienceOn
15 Carr, J.C., Fright, W.R. and Beatson, R.K. (2001), "Surface interpolation with radial basis functions for medical imaging", Comput. Graph. Proc., Annu. Conf. Ser., 67-76.
16 Nevis, A. (1999), "Adaptive background equalization and image processing applications for laser line scan data". SPIE 3710.
17 Jaffe, J.S. (1998), "Underwater optical imaging: the design of optimal systems", Oceanography, 11(1), 40-41.
18 Jaffe, J.S. (2001), "Underwater optical imaging: status and prospects", Oceanography, 14(3), 64-75.   DOI   ScienceOn
19 Kovesi, P. (1999), "Image features from phase congruency", J. Comput. Vision Res., 1(3), 1-27.
20 Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008a), "Edge detection via a fast and adaptive bidimensional empirical mode decomposition", Proceedings of the Machine Learning for Signal Processing. MLSP 2008.
21 Bhuiyan, S.M.A., Adhami, R.R. and Khan, J.F. (2008b), "Fast and adaptive bidimensional empiricalmode decomposition using order-statistics filter based envelope estimatio", EURASIP Journal on Advances in Signal Processing.