Corner Detection of Contour Images using Spectral Clustering
Corner detection plays an important role in object recognition and motion analysis. In this paper, we propose a hierarchical corner detection framework based on spectral clustering (SC). The framework consists of three stages: contour smoothing, corner cell extraction and corner localization. In the contour smoothing stage, wavelet decomposition is imposed on the raw contour to reduce noise. In the corner cell extraction stage, several atomic corner cells are obtained by SC. In the corner localization stage, the corner points of each corner cell are located by the corner locator based on the kernel-weighted cosine curvature measure. Experimental results demonstrate the superiority of our framework.