Revision of Using Eigenvalues of Covariance Matrices in Boundary-Based Corner Detection

Wen-Bing HORNG  Chun-Wen CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D    No.9    pp.1692-1701
Publication Date: 2009/09/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1692
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
corner detection,  covariance matrix,  curvature estimation,  eigenvalue,  measure of significance,  

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In this paper, we present a revision of using eigenvalues of covariance matrices proposed by Tsai et al. as a measure of significance (i.e., curvature) for boundary-based corner detection. We first show the pitfall of Tsai et al.'s approach. We then further investigate the properties of eigenvalues of covariance matrices of three different types of curves and point out a mistake made by Tsai et al.'s method. Finally, we propose a modification of using eigenvalues as a measure of significance for corner detection to remedy their defect. The experiment results show that under the same conditions of the test patterns, in addition to correctly detecting all true corners, the spurious corners detected by Tsai et al.'s method disappear in our modified measure of significance.