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QWT: Retrospective and New Applications

  • Yi Xu
  • Xiaokang Yang
  • Li Song
  • Leonardo TraversoniEmail author
  • Wei Lu
Chapter

Abstract

Quaternion wavelet transform (QWT) achieves much attention in recent years as a new image analysis tool. In most cases, it is an extension of the real wavelet transform and complex wavelet transform (CWT) by using the quaternion algebra and the 2D Hilbert transform of filter theory, where analytic signal representation is desirable to retrieve phase-magnitude description of intrinsically 2D geometric structures in a grayscale image. In the context of color image processing, however, it is adapted to analyze the image pattern and color information as a whole unit by mapping sequential color pixels to a quaternion-valued vector signal. This paper provides a retrospective of QWT and investigates its potential use in the domain of image registration, image fusion, and color image recognition. It is indicated that it is important for QWT to induce the mechanism of adaptive scale representation of geometric features, which is further clarified through two application instances of uncalibrated stereo matching and optical flow estimation. Moreover, quaternionic phase congruency model is defined based on analytic signal representation so as to operate as an invariant feature detector for image registration. To achieve better localization of edges and textures in image fusion task, we incorporate directional filter bank (DFB) into the quaternion wavelet decomposition scheme to greatly enhance the direction selectivity and anisotropy of QWT. Finally, the strong potential use of QWT in color image recognition is materialized in a chromatic face recognition system by establishing invariant color features. Extensive experimental results are presented to highlight the exciting properties of QWT.

Keywords

Local Binary Pattern Stereo Match Quaternion Algebra Phase Congruency Contourlet Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2010

Authors and Affiliations

  • Yi Xu
    • 1
  • Xiaokang Yang
    • 1
  • Li Song
    • 1
  • Leonardo Traversoni
    • 2
    Email author
  • Wei Lu
    • 1
  1. 1.Institute of Image Communication and Information Processing, Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Ciencias Básicas e IngenieriaUniv. Autonoma Met. (Iztapalapa)MexicoMexico

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