Feature Point Extraction from the Local Frequency Map of an Image

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