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  • Research Article
  • Open Access

Feature Point Detection Utilizing the Empirical Mode Decomposition

EURASIP Journal on Advances in Signal Processing20082008:287061

  • Received: 22 June 2007
  • Accepted: 3 March 2008
  • Published:


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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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
Department of Electrical and Computer Engineering, University of Delaware, Delaware, DE 19716, USA


© 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.