Oriented asymmetric kernels for corner detection

Corners and junctions play an important role in many image analysis applications. Nevertheless, these features extracted by the majority of the proposed algorithms in the literature do not correspond to the exact position of the corners. In this paper, an approach for corner detection based on the combination of different asymmetric kernels is proposed. Informations captured by the directional kernels enable to describe precisely all the grayscale variations and the directions of the crossing edges around the considered pixel. Compared to other corner detection algorithms on synthetic and real images, the proposed approach remains more stable and robust to noise than the comparative methods.