Feature Point Detection Utilizing the Empirical Mode Decomposition

  • Jesmin Farzana Khan
  • Kenneth Barner
  • Reza Adhami
Open Access
Research Article
Part of the following topical collections:
  1. The Empirical Mode Decomposition and the Hilbert-Huang Transform


This paper introduces a novel contour-based method for detecting largely affine invariant interest or feature points. In the first step, image edges are detected by morphological operators, followed by edge thinning. In the second step, corner or feature points are identified based on the local curvature of the edges. The main contribution of this work is the selection of good discriminative feature points from the thinned edges based on the 1D empirical mode decomposition (EMD). Simulation results compare the proposed method with five existing approaches that yield good results. The suggested contour-based technique detects almost all the true feature points of an image. Repeatability rate, which evaluates the geometric stability under different transformations, is employed as the performance evaluation criterion. The results show that the performance of the proposed method compares favorably against the existing well-known methods.


Repeatability Rate Feature Point Empirical Mode Decomposition Full Article Point Detection 
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Copyright information

© Jesmin Farzana Khan et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Jesmin Farzana Khan
    • 1
  • Kenneth Barner
    • 2
  • Reza Adhami
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of Alabama in HuntsvilleHuntsvilleUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of DelawareDelawareUSA

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