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A Flexible Framework for Local Phase Coherence Computation

  • Rania Hassen
  • Zhou Wang
  • Magdy Salama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6753)

Abstract

Local phase coherence (LPC) is a recently discovered property that reveals the phase relationship in the vicinity of distinctive features between neighboring complex filter coefficients in the scale-space. It has demonstrated good potentials in a number of image processing and computer vision applications, including image registration, fusion and sharpness evaluation. Existing LPC computation method is restricted to be applied to three coefficients spread in three scales in dyadic scale-space. Here we propose a flexible framework that allows for LPC computation with arbitrary selections in the number of coefficients, scales, as well as the scale ratios between them. In particular, we formulate local phase prediction as an optimization problem, where the object function computes the closeness between true local phase and the predicted phase by LPC. The proposed method not only facilitates flexible and reliable computation of LPC, but also demonstrates strong robustness in the presence of noise. The groundwork laid here broadens the potentials of LPC in future applications.

Keywords

local phase coherence scale-space complex wavelet coefficients feature detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rania Hassen
    • 1
  • Zhou Wang
    • 1
  • Magdy Salama
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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