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Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution Cover

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

By: Ewa Świercz  
Open Access
|Mar 2010

References

  1. Auger F., Flandrin P., Goncalves P. and Lemoine O. (1996). Time-Frequency Toolbox for Matlab, CNRS, Rice University, Houston, TX http://iut-saint-nazaire.univ-nantes.fr/~{}auger/tftb.html
  2. Basri R., Costa L., Geiger D. and Jacobs D. (1998). Determining the similarity of deformable shapes, Vision Research 38(15-16): 2365-2385.10.1016/S0042-6989(98)00043-1
  3. Basseville M. (1989). Distance measures for signal processing and pattern recognition, Signal Processing 35(3): 349-369.10.1016/0165-1684(89)90079-0
  4. Belongie S., Malik J. and Puzicha J. (2002). Shape matching and object recognition using shape contexts, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4): 509-522.10.1109/34.993558
  5. Bishop C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer Science + Business Media LLC, New York, NY.
  6. Breakenridge C. and Mesbah M. (2003). Minimum classification error using time-frequency analysis, Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2003), Darmstad, Germany, pp. 717-720.
  7. Colas M. and Gelle G. (2004). A multitime-frequency approach for detection and classification of neighboring instantaneous frequency laws in a noisy environment, Signal Processing Letters 11(2): 71-74.10.1109/LSP.2003.821656
  8. Davy M. and Doncarli C. (1998). Optimal kernels of time-frequency representations for signal classification, Proceedings of the International Symposium Time-Frequency and Time-Scale, Pittsburgh, PA, USA, pp. 581-584.
  9. Demirci M. F., van Leuken R. H. and Veltkamp R. C. (2007). Shape indexing through laplacian spectra, Proceedings of the International Conference on Image Analysis and Processing Workshops (ICIAPW 2007), Modena, Italy, pp. 21-26.
  10. Doncarli C., Davy M. and Boudreaux-Bartels F. (2001). Improved optimization of time-frequency-based signal classifiers, IEEE Signal Processing Letters 8(2): 52-57.10.1109/97.895373
  11. Duda R. O., Hart P. E. and Stork D. G. (2001). Pattern Classification, 2nd Edition, John Wiley & Sons, Inc., New York, NY.
  12. Flandrin P. (1988). A time-frequency formulation of optimal detection, IEEE Transactions on Acoustics, Speech and Signal Processing 36(9): 1337-1384.10.1109/29.90365
  13. Fry D. (1993). Shape Recognition Using Metrics on the Space of Shapes, Ph.D. thesis, Harvard University, Cambridge, MA.
  14. Fukunaga K. (1990). Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press, London.10.1016/B978-0-08-047865-4.50007-7
  15. Gdalyahu Y. and Weinshall D. (1999). Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(12): 1312-1328.10.1109/34.817410
  16. Gillespie B. and Atlas L. (2001). Optimizing time-frequency kernels for classification, IEEE Transactions on Signal Processing 49(3): 485-496.10.1109/78.905863
  17. Grigorescu S. E., Petkov N. and Kruizinga P. (2002). Comparison of texture features based on Gabor filters, IEEE Transactions on Image Processing 11(10): 1160-1167.10.1109/TIP.2002.80426218249688
  18. Gröchenig K. (2001). Foundations of Time-Frequency Analysis, Birkhäuser, Boston, MA, pp. 83-142.
  19. Hagedoorn M. and Veltkamp R. C. (1999). Reliable and efficient pattern matching using an affine invariant metric, Journal of Computer Vision 31(2/3): 203-225.10.1023/A:1008022116857
  20. Heitz C. (1995). Optimum time-frequency representations for the classification and detection of signals, Applied Signal Processing 2(3): 124-143.
  21. Huang Y., Chan K. L. and Zhang Z. (2003). Texture classification by multi-model feature integration using Bayesian networks, Pattern Recognition Letters 24(1-3): 393-401.10.1016/S0167-8655(02)00263-5
  22. Jain A. K., Duin R. P. W. and Mao J. (2000). Statistical pattern recognition: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1): 4-7.10.1109/34.824819
  23. Kyrki V., Kamarainen J.-K. and Klviinen H. (2004). Simple Gabor feature space for invariant object recognition, Pattern Recognition Letters 25(3): 311-318.10.1016/j.patrec.2003.10.008
  24. Latecki L. J. and Lakamper R. (2000). Shape similarity measure based on correspondence of visual parts, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10): 1185-1190.10.1109/34.879802
  25. Li S. and Shawe-Taylor S. (2005). Comparison and fusion of multiresolution features for texture classification, Pattern Recognition Letters 26(5): 633-638.10.1016/j.patrec.2004.09.013
  26. Liu H. and Srinath M. (1990). Partial shape classification using contour matching in distance transforms, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(2): 1072-1079.10.1109/34.61706
  27. Manay S., Cremers D., Hong B.-W., Yezzi A. J. Jr. and Soatto S. (2006). Integral invariants for shape matching, IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10): 1602-1618.10.1109/TPAMI.2006.20816986542
  28. McLachlan G. J. (1992). Discriminant Analysis and Statistical Pattern Recognition, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., New York, NY.
  29. Petrakis E. G. M., Diplaros A. and Milios E. (2002). Matching and retrieval of distorted and occluded shapes using dynamic programming, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11): 1501-1516.10.1109/TPAMI.2002.1046166
  30. Qian S. and Chen D. (1993). Discrete Gabor Transform, IEEE Transactions on Signal Processing 41(7): 2429-2438.10.1109/78.224251
  31. Richard C. and Lengell R. (1999). Data driven design and complexity control of time frequency detectors, Signal Processing 77(1): 37-48.10.1016/S0165-1684(99)00021-3
  32. Santini S. and Jain R. (1999). Similarity measures, IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9): 871-883.10.1109/34.790428
  33. Sebe N. and Lew M. S. (2002). Maximum likelihood shape matching, Proceedings of the 5th Asian Conference on Computer Vision (ACCV2002), Melbourne, Australia, Vol. 1, pp. 713-718.
  34. Sejdic E., Djurovic I. and Jiang J. (2009). Time-frequency feature representation using energy concentration: An overview of recent advances, Digital Signal Processing 19(1): 153-183.10.1016/j.dsp.2007.12.004
  35. Sondergaard P. (2006). Time-Frequency Toolbox for Matlab, Technical University of Denmark, Lyngby, http://www2.mat.dtu.dk/people/P.Soendergaard/toolbox/. http://www2.mat.dtu.dk/people/P.Soendergaard/toolbox/
  36. Tai C.-F. (2007). Image mining by spectral features: A case study of scenery image classification, Expert Systems with Applications 32(1): 135-142.10.1016/j.eswa.2005.11.016
  37. Umeyama S. (1993). Parameterized point pattern matching and its application to recognition of object families, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(2): 136-144.10.1109/34.192485
  38. Veltkamp R. C. (2001). Shape matching: Similarity measures and algorithms, Technical Report UU-CS-2001-03, Utrecht University, Utrecht.
  39. Vincent I., Doncarli C. and Carpentier E. L. (1994). Nonstationary signals classification using time-frequency distributions, Proceedings of the International Symposium on Time-Frequency and Time Scale, Paris, France, pp. 233-236.
  40. Werther T., Eldar Y. C. and Subanna N. K. (2005). Dual Gabor frames: Theory and computational aspects, IEEE Transactions on Signal Processing 53(11): 4147-4158.10.1109/TSP.2005.857049
  41. Xie J., Hengb P.-A. and Shah M. (2008). Shape matching and modeling using skeletal context, Pattern Recognition 41(5): 1773-1784.10.1016/j.patcog.2007.11.005
  42. Younes L. (1999). Optimal matching between shapes via elastic deformations, Image and Vision Computing 17(5-7): 381-389.10.1016/S0262-8856(98)00125-5
  43. Zhang D. and Lu G. (2003). A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval, Journal of Visual Communication and Image Representation 14(1): 41-60.10.1016/S1047-3203(03)00003-8
  44. Zhang D. and Lu G. (2004). Review of shape representation and description techniques, Pattern Recognition 37(1): 1-19.10.1016/j.patcog.2003.07.008
DOI: https://doi.org/10.2478/v10006-010-0010-x | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 135 - 147
Published on: Mar 25, 2010
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2010 Ewa Świercz, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 20 (2010): Issue 1 (March 2010)