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.
Highlights
There are a wide variety of methods reported in the literature for interest point and corner detection in greylevel images
Results of experiments conducted to test the efficacy of the proposed corner detection algorithm are provided
This research presents a robust, rotation invariant, and scaleinvariant corner detection scheme for images based on the morphological edge detection, the eigenvectors of covariance matrices for boundary segment points, and the 1D empirical mode decomposition (EMD)
Summary
There are a wide variety of methods reported in the literature for interest point and corner detection in greylevel images. Contour-based methods have existed for some time [1,2,3,4,5,6]. Corner detection schemes using the wavelet transform (WT) are popular due to the fact that the WT is able to decompose an input signal into smooth and detailed parts by low-pass and high-pass filters at multiresolution levels [7]. In this manner, local deviations are captured at various detailed decomposition levels. Several wavelet-based approaches are reported in [8,9,10,11,12,13,14,15]
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