A novel texture classification method using multi-directions main frequency center

Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR '07

Volumn 3, Issue , 2007, Pages 1372-1376

  Yang, Zhihua ^a   Yang, Lihua ^b  

^a GUANGDONG UNIVERSITY OF BUSINESS STUDIES   (China)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

Empirical mode decomposition (EMD); Hilbert Huang transform (HHT); Main frequency center; Texture

Indexed keywords

MATHEMATICAL TRANSFORMATIONS; PATTERN RECOGNITION; STATISTICAL TESTS; WAVELET ANALYSIS;

BRODATZ ALBUMS; EMPIRICAL MODE DECOMPOSITION; FEATURE VECTORS; HILBERT HUANG TRANSFORMS; MAIN FREQUENCY; MINIMUM DISTANCE CLASSIFIERS; TEXTURE CLASSIFICATION; TEXTURE IMAGE;

TEXTURES;

EID: 45149090361     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICWAPR.2007.4421648     Document Type: Conference Paper

References (16)

1. P.C.Chen, and T.Pavlidis. Segmentation by texture using correlation. IEEE Trans.Pattern Anal. Machine Intell., 5:64-69, 1983.
2. A.C.Bovik Analysis of multichannel narrow band filters for image texture segment. IEEE Trans. Signal Processing, 39:2025-2043, 1991.
3. T. Randen, and J. H. Husoy. Filtrting for texture classification: A comparative study. IEEE Trans. Pattern Anal. Machine Intell., 21(4):291-310, 1999.
4. M.Unser, texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing, 4(11):1549-1560, 1995.
5. D. Charalampidis,and T. Kasparis Wavelet-Based rotational invariant roughness features for texture classification and segmentation, IEEE Trans. Image Processing, 11(8):825-837, 2002.
6. C. M. Pun,and M. C. Lee Log-Polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans. Pattern Anal. Machine Intell., 25(5):590-603, 2003.
7. K. J. Khouzani,and H. S. Zadeh Rotation-invariant multiresolution texture classification using Radon and wavelet transform. IEEE Trans. Image Processing, 14(6):783-759, 2005.
8. B. S. Manjunath, and W. Y. Ma. Texture Features for Browsing and Retrieval of image Data. IEEE Trans. Pattern Anal. Machine Intell., 18(8):837-842, 1996.
9. N. E. Huang, Z. Shen, and S. R. Long et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, A(454):903-995, 1998.
10. C. H. Loh, T. C. Wu, and N. E. Huang. Application of emd+hht method to identify near-fault ground motion characteristics and structural responses. BSSA, Special Issue of Chi-Chi Earthquake, 91(5):1339-1357, 2001.
11. J. C. Nunes, Y. Bouaoune, E. Delechelle, S. Guyot, and Ph. Bunel, Texture analysis based on the bidimensional empirical mode decomposition. Machine Vision and Application, 2003.
12. S. C. Phillips, R. J. Gledhill, J. W. Essex, and C. M.Edge. Application of the Hilbert-Huang Transform to the analysis of molecular dynamic simulations. J. Phys. Chem., A(107):4869-4876, 2003.
13. Zhihua Yang, Dongxu Qi, and Lihua Yang. Detecting Pitch Period Based on Hilbert-Huang Transform. Chinese Journal of Computers, 29(1): 106-115,2006.
14. Zhihua Yang, Dongxu Qi, and Lihua Yang. Chinese Font Recognition Based on EMD. Pattern Recognition Letters, 27(14): 1692-1701,2006.
15. Zhihua Yang, Lihua YangDong, and xu Qi. Detection of spindles in sleep EEGs using a novel algorithm based on the Hilbert-Huang transform. Book on Applied and Numerical Harmonic Analysi, 543-559,2006.
16. Zhihua Yang, Dongxu Qi, Lihua Yang, and Lijun Wu. An Approach to Analyze the Period of a Signal Based on HHT and Its Application. ACTA Scientiarum Naturalium Universitatis Sun Yat-Seni, 44(2):1418,2005.