A Corner Classification Algorithm Based on Directed Areas
基于有向面积的角点分类算法

In the domain of 3D scene reconstruction,motion estimate and machine vision,we need to detect the corners in a image, as well as obtain more information around corners,namely we could classify the corner further.We proposed a corner classification method based on directed area.First,we applied corner detection principle of wavelet transformation,based on the eigenvectors of covariance matrices,to detect the corner on the edge of images.Secondly,we defined six types of corners based on the edge information of both sides.Finally,corners can be classified by computing three directed areas arranged in order on the edge of the corners.The experiment indicated that the method of classifying corners has higher accuracy.