Analysis of Dynamic Processes by Statistical Moments of High Orders

  • Stanislava Šimberová
  • Tomáš Suk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

We present a new approach to image analysis in temporal sequence of images (data cube). Our method is based on high-order statistical moments (skewness and kurtosis) giving interesting information about a dynamic event in the temporal sequence. The moments enable precise determination of the ”turning points” in the temporal sequence of images. The moment’s curves are analyzed by continuous complex Morlet wavelet that leads to the description of quasi-periodic processes in the investigated event as a time sequence of local spectra. These local spectra are compared with Fourier spectrum. We experimentally illustrate the performance on the real data from astronomical observations.

Keywords

Statistical moments Frequency analysis Fourier and wavelet transformations Dynamic processes 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stanislava Šimberová
    • 1
  • Tomáš Suk
    • 2
  1. 1.Astronomical InstituteAcademy of Sciences of the Czech RepublicOndřejovCzech Republic
  2. 2.Institute of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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