Transformation de wigner-ville: description d’un nouvel outil de traitement du signal et des images

AnalyseLa transformation de Wigner-Ville a fait l’objet, ces dernières années, d’un grand nombre de travaux en traitement du signal et ceci aussi bien sur le plan théorique que dans le domaine des applications. Le but de cet article est de montrer les possibilités offertes par cette transformation, dans sa version discrète, pour les traitements numériques en temps réel de signaux monodimensionnels et des images à 2 ou 3 dimensions.AbstractDuring the last years the Wigner-Ville transform has had an increasing interest in the field of signal processing, as well in theory than in applications. The purpose of this paper is to show the possibilities given by the discrete version of this transformation for real-time processing of monodimensional signals and 2D or 3D images.

[1]  W. Martin,et al.  Time-frequency analysis of random signals , 1982, ICASSP.

[2]  Theo A. C. M. Claasen,et al.  On positivity of time-frequency distributions , 1985, IEEE Trans. Acoust. Speech Signal Process..

[3]  Cornelis P. Janse,et al.  Time-Frequency Distributions of Loudspeakers: The Application of the Wigner Distribution , 1983 .

[4]  E. Wigner On the quantum correction for thermodynamic equilibrium , 1932 .

[5]  Theo A. C. M. Claasen,et al.  On the time-frequency discrimination of energy distributions: Can they look sharper than Heisenberg ? , 1984, ICASSP.

[6]  Harry Wechsler,et al.  A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images , 1982, Pattern Recognit. Lett..

[7]  Patrick Flandrin,et al.  Wigner-Ville spectral analysis of nonstationary processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[8]  Mj Martin Bastiaans The Wigner distribution function applied to optical signals and systems , 1978 .

[9]  A. Lohmann,et al.  The wigner distribution function and its optical production , 1980 .

[10]  Patrick Flandrin,et al.  Some features of time-frequency representations of multicomponent signals , 1984, ICASSP.

[11]  David S. K. Chan,et al.  A non-aliased discrete-time Wigner distribution for time-frequency signal analysis , 1982, ICASSP.

[12]  Patrick Flandrin,et al.  An interpretation of the Pseudo-Wigner-Ville distribution , 1984 .

[13]  Tariq S. Durrani,et al.  Systolic processor for computing the Wigner distribution , 1983 .

[14]  P. Flandrin,et al.  Time and frequency representation of finite energy signals: A physical property as a result of an hilbertian condition , 1980 .

[15]  Bernard Escudie,et al.  Représentation en temps et fréquence des signaux d’énergie finie: analyse et observation des signaux , 1979 .

[16]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[17]  Françoise Peyrin,et al.  A unified definition for the discrete-time, discrete-frequency, and discrete-time/Frequency Wigner distributions , 1986, IEEE Trans. Acoust. Speech Signal Process..

[18]  P. Flandrin,et al.  Detection of changes of signal structure by using the Wigner-Ville spectrum , 1985 .

[19]  T. Claasen,et al.  The aliasing problem in discrete-time Wigner distributions , 1983 .

[20]  Ben R. Breed,et al.  A range and azimuth estimator based on forming the spatial Wigner distribution , 1984, ICASSP.