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2015 | 25 | 4 | 927-941
Tytuł artykułu

Application of cubic box spline wavelets in the analysis of signal singularities

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian) or the quadratic box spline are commonly used for the task of singularity detection. The disadvantage of the Mexican hat, however, is its unlimited support; the disadvantage of the quadratic box spline is a phase shift introduced by the wavelet, making it difficult to locate singular points. The paper deals with the construction and properties of wavelets in the form of cubic box splines which have compact and short support and which do not introduce a phase shift. The digital filters associated with cubic box wavelets that are applied in implementing the discrete dyadic wavelet transform are defined. The filters and the algorithme à trous of the discrete dyadic wavelet transform are used in detecting signal singularities and in calculating the measures of signal singularities in the form of a Lipschitz exponent. The article presents examples illustrating the use of cubic box spline wavelets in the analysis of signal singularities.
Rocznik
Tom
25
Numer
4
Strony
927-941
Opis fizyczny
Daty
wydano
2015
otrzymano
2014-06-27
poprawiono
2015-04-03
Twórcy
  • Faculty of Management, Białystok University of Technology, ul. Stefana Tarasiuka 2, 16-001 Kleosin, Poland
Bibliografia
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  • Mallat, S. (1991). Zero-crossings of a wavelet transform, IEEE Transactions on Information Theory 37(4): 1019-1033.
  • Mallat, S. (2009). A Wavelet Tour of Signal Processing: The Sparce Way, Third Edition, Academic Press, Burlington, MA.
  • Mallat, S. and Hwang, L.W. (1992). Singularity detection and processing with wavelets, IEEE Transactions on Information Theory 38(2): 617-643.
  • Mallat, S. and Zhong, S. (1992). Characterization of signals from multiscale edges, IEEE Transactions on Pattern Analysis and Machine Intelligence 14(7): 710-732.
  • Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (2007). Numerical Recipes The Art of Scientific Computing, Cambridge University Press, Cambridge.
  • Rakowski, W. (2003). A proof of the necessary condition for perfect reconstruction of signals using the two-channel wavelet filter bank, Bulletin of the Polish Academy of Science: Technical Sciences 51(1): 14-23.
  • Shensa, M.J. (1992). The discrete wavelet transform: Wedding the à trous and Mallat algorithms, IEEE Transactions on Signal Processing 40(10): 2464-2482.
  • Skodras, A., Christopoulos, C. and Ebrahimi, T. (2001). The JPEG 2000 still image compression standard, IEEE Signal Processing Magazine 18(5): 36-58.
  • Tu, C.-L. and Hwang, W.-L. (2005). Analysis of singularities from modulus maxima of complex wavelets, IEEE Transactions on Information Theory 51(3): 1049-1062.
  • Unser, M. (1999). Splines: A perfect fit for signal and image processing, IEEE Signal Processing Magazine 16(6): 22-38.
  • Unser, M. and Blu, T. (2003). Mathematical properties of the JPEG2000 wavelet filters, IEEE Transactions on Image Processing 12(9): 1080-1090.
  • Witkin, A.P. (1983). Scale-space filtering, Proceedings of the International Conference on Artificial Intelligence, Karlsruhe, Germany, pp. 1019-1022.
  • Witkin, A.P. (1984). Scale-space filtering: A new approach to multi-scale description, in S. Ullman and W. Richards (Eds.), Image Understanding, Ablex, Norwood, NJ.
  • Zhao, Y., Hu, J., Zhang, L. and Liao, T. (2013). Mallat wavelet filter coefficient calculation, 5th International Conference on Computational and Information Sciences, Shiyan, Hubei, China, pp. 963-965.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv25i4p927bwm
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