Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2012, Article ID 182309, 15 pages
Research Article

Feature Point Extraction from the Local Frequency Map of an Image

1Department of Electrical Engineering, Tuskegee University, Tuskegee, AL 36088, USA
2Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA

Received 18 March 2011; Accepted 12 December 2011

Academic Editor: Tamal Bose

Copyright © 2012 Jesmin Khan et al. 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.


We propose a novel technique for detecting rotation- and scale-invariant interest points from the local frequency representation of an image. Local or instantaneous frequency is the spatial derivative of the local phase, where the local phase of any signal can be found from its Hilbert transform. Local frequency estimation can detect edge, ridge, corner, and texture information at the same time, and it shows high values at those dominant features of an image. For each pixel, we select an appropriate width of the window for computing the derivative of the phase. In order to select the width of the window for any given pixel, we make use of the measure of the extent to which the phases, in the neighborhood of that pixel, are in the same direction. The local frequency map, thus obtained, is then thresholded by employing a global thresholding approach to detect the interest or feature points. Repeatability rate, a performance evaluation criterion for an interest point detector, is used to check the geometric stability of the proposed method under different transformations. We present simulation results of the detection of feature points from image utilizing the suggested technique and compare the proposed method with five existing approaches that yield good results. The results prove the efficacy of the proposed feature point detection algorithm. Moreover, in terms of repeatability rate; the results show that the performance of the proposed method with respect to different aspect is compatible with the existing methods.