Skip to main content


You are viewing the new BMC article page. Let us know what you think. Return to old version

Research Article | Open | Published:

Feature Point Detection Utilizing the Empirical Mode Decomposition


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.

Publisher note

To access the full article, please see PDF.

Author information

Correspondence to Jesmin Farzana Khan.

Rights and permissions

Reprints and Permissions

About this article


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