Feature Point Detection Utilizing the Empirical Mode Decomposition. (11th August 2008)

• Record Type: Journal Article
• Authors: Khan Khan, Jesmin Farzana Jesmin Farzana; Barner Barner, Kenneth Kenneth; Adhami Adhami, Reza Reza
• Other Names: Zhang Zhang Ray Ray Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2008-08-11
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/287061
• Languages: English
• ISSNs: 1687-6172

Abstract

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.