Wavelet-based corner detection using eigenvectors of covariance matrices

Authors:
Chi-Hao Yeh
Affiliations:
Department of Industrial Engineering and Management, National Taipei University of Technology, No. 1, Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

The proposed approach in this paper is to detect true corners and avoid false alarms on circular arcs by using the eigenvectors of covariance matrices and one-dimensional wavelet transform (1-D WT). The 2-D boundaries of an object are initially represented by the 1-D tangent angles calculated by the eigenvectors of covariance matrix from the boundary points coordinates over a small boundary segment. Since true corners result in stronger tangent variations, 1-D WT can be utilized to decompose the 1-D tangent angles and capture the irregular angle variations. In this manner, the locations of true corners can be easily identified by comparing the 1-D WT wavelet coefficients at high-pass decomposition levels with a pre-defined threshold. Experimental results show that the proposed method is invariant to rotation and scale under appropriate image resolution and adequate region of support for covariance matrices.