Fast and adaptive bidimensional empirical mode decomposition using order-statistics filter based envelope estimation

Eurasip Journal on Advances in Signal Processing

Volumn 2008, Issue , 2008, Pages

  Bhuiyan, Sharif M A ^a   Adhami, Reza R ^a   Khan, Jesmin F ^a  

^a UNIVERSITY OF ALABAMA IN HUNTSVILLE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; ESTIMATION; HIERARCHICAL SYSTEMS; SINGULAR VALUE DECOMPOSITION; STATISTICAL METHODS; TWO DIMENSIONAL; WAVE FILTERS;

BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION; COMPUTATION TIME; FILTER BASED; FILTER SIZES; HIERARCHICAL COMPONENTS; INTRINSIC MODE FUNCTIONS; LOCAL MAXIMUM; LOWER ENVELOPES; ORDER STATISTICS FILTERS; ORDER-STATISTICS; SCATTERED DATA INTERPOLATION; SIMULATION RESULTS; SURFACE INTERPOLATION;

INTERPOLATION;

EID: 49749119531     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1155/2008/728356     Document Type: Article

References (24)

1. Huang N. E., Shen Z., Long S. R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proceedings of the Royal Society A 1998 454 1971 903 995
2. Huang N. E., Shen Z., Long S. R., A new view of nonlinear water waves: the Hilbert spectrum Annual Review of Fluid Mechanics 1999 31 417 457
3. Nunes J. C., Bouaoune Y., Delchelle E., Niang O., Bunel Ph., Image analysis by bidimensional empirical mode decomposition Image and Vision Computing 2003 21 12 1019 1026
4. Nunes J. C., Guyot S., Delchelle E., Texture analysis based on local analysis of the bidimensional empirical mode decomposition Machine Vision and Applications 2005 16 3 177 188
5. Linderhed A., 2-D empirical mode decompositions in the spirit of image compression 4738 Wavelet and Independent Component Analysis Applications IX April 2002 Orlando, Fla, USA 1 8 Proceedings of SPIE
6. Damerval C., Meignen S., Perrier V., A fast algorithm for bidimensional EMD IEEE Signal Processing Letters 2005 12 10 701 704
7. Xu Y., Liu B., Liu J., Riemenschneider S., Two-dimensional empirical mode decomposition by finite elements Proceedings of the Royal Society A 2006 462 2074 3081 3096
8. Liu Z., Wang H., Peng S., Texture classification through directional empirical mode decomposition 4 Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 04) August 2004 Cambridge, UK 803 806
9. Liu Z., Wang H., Peng S., Texture segmentation using directional empirical mode decomposition 1 Proceedings of IEEE International Conference on Image Processing (ICIP 04) October 2004 Singapore 279 282
10. Long S. R., Huang N. E., Shen S. S. P., Applications of HHT in image analysis Hilbert-Huang Transform and Its Applications 2005 River Edge, NJ, USA World Scientific
11. Liu Z., Peng S., Boundary processing of bidimensional EMD using texture synthesis IEEE Signal Processing Letters 2005 12 1 33 36
12. Huang N. E., Wu M.-L. C., Long S. R., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis Proceedings of the Royal Society A 2003 459 2037 2317 2345
13. Rilling G., Flandrin P., Gonalves P., On empirical mode decomposition and its algorithms Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP 03) June 2003 Grado, Italy
14. Junsheng C., Dejie Y., Yu Y., Research on the intrinsic mode function (IMF) criterion in EMD method Mechanical Systems and Signal Processing 2006 20 4 817 824
15. Shen M., Tang H., Li B., The modified bidimensional empirical mode decomposition for image denoising 4 Proceedings of the 8th IEEE International Conference on Signal Processing (ICSP 06) November 2007 Beijing, China
16. Washizawa Y., Tanaka T., Mandic D. P., Cichocki A., Gabrys B., Howlett R. J., Jain L. C., A flexible method for envelope estimation in empirical mode decomposition Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 06) October 2006 Bournemouth, UK 1248 1255
17. Srinivasan R., Rengaswamy R., Miller R., A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops Control Engineering Practice 2007 15 9 1135 1148
18. Peng Z. K., Tse P. W., Chu F. L., An improved Hilbert-Huang transform and its application in vibration signal analysis Journal of Sound and Vibration 2005 286 1-2 187 205
19. Xuan B., Xie Q., Peng S., EMD sifting based on bandwidth IEEE Signal Processing Letters 2007 14 8 537 540
20. Liu Z., Huang D., Li K., Irwin G. W., A novel boundary extension approach for empirical mode decomposition Proceedings of the International Conference on Intelligent Computing (ICIC 06) August 2006 Kunming, China 299 304
21. Shen B., Estimating the instantaneous frequencies of a multicomponent AM-FM image by bidimensional empirical mode decomposition Proceedings of IEEE International Workshop on Intelligent Signal Processing (WISP 05) September 2005 Faro, Portugal 283 287
22. Kizhner S., Blank K., Flatley T., Huang N. E., Petrick D., Hestnes P., On certain theoretical developments underlying the Hilbert-Huang transform Proceedings of IEEE Aerospace Conference March 2006 Big Sky, Mont, USA 14
23. Gonzalez R. C., Woods R. E., Digital Image Processing 2002 2nd Upper Saddle River, NJ, USA Pearson Education
24. Brodatz P., Textures: A Photographic Album for Artists and Designers 1966 New York, NY, USA Dover