Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique

Many contour-based image corner detectors are based on the curvature scale-space (CSS). We identify the weaknesses of the CSS-based detectors. First, the curvature itself by its definition is very much sensitive to the local variation and noise on the curve, unless an appropriate smoothing is carried out beforehand. In addition, the calculation of curvature involves derivatives of up to second order, which may cause instability and errors in the result. Second, the Gaussian smoothing causes changes to the curve and it is difficult to select an appropriate smoothing-scale, resulting in poor performance of the CSS corner detection technique. We propose a complete corner detection technique based on the chord-to-point distance accumulation (CPDA) for the discrete curvature estimation. The CPDA discrete curvature estimation technique is less sensitive to the local variation and noise on the curve. Moreover, it does not have the undesirable effect of the Gaussian smoothing. We provide a comprehensive performance study. Our experiments showed that the proposed technique performs better than the existing CSS-based and other related methods in terms of both average repeatability and localization error.