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Feature Point Detection Utilizing the Empirical Mode Decomposition

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Abstract

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.

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Correspondence to Jesmin Farzana Khan.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Khan, J.F., Barner, K. & Adhami, R. Feature Point Detection Utilizing the Empirical Mode Decomposition. EURASIP J. Adv. Signal Process. 2008, 287061 (2008) doi:10.1155/2008/287061

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Keywords

  • Repeatability Rate
  • Feature Point
  • Empirical Mode Decomposition
  • Full Article
  • Point Detection