Corner Detection Based on a Rotation-Invariant and Noise-Insensitive Curvature Measurement

Xun Sun, Baojiang Zhong, Kai-Kuang Ma

Abstract: Corner detection is extensively applied across various computer vision tasks. Current corner detectors typically assume that the distance between every two nearby pixels is constant. However, this assumption is invalid in real-world scenarios. As a result, the pixel-based curvature measurements designed and used in these corner detectors may suffer instability under rotation transformation and noise interference. To tackle this issue, a novel curvature measurement is proposed in this paper, which exploits the length of the subpixelized chord to estimate the discrete curvatures of digital curves. The proposed curvature model is invariant to rotation transformation and insensitive to image noise interference. Based on this curvature measurement, a new corner detector is further developed. Experimental results demonstrate that our proposed corner detector outperforms existing state-of-the-art methods.