Behavior Analysis And Detection Research Of Corner In Contour Scale Space

Image local features retain important information, which reduce the data quantity of image processing effectively, and improve the operation speed greatly. Thus, feature extraction has become the basic research of the related areas of image processing such as pattern recognition and computer vision. Among the many image features, not only do the corners have the least amount of data, but also they are quite stable, which makes corner detection as an important branch of feature extraction. However, most of the corner detection algorithms are merely implemented in the experimental level, lacking of mathematical demonstration. Based on above, this paper will investigate the evolution behavior of corners in the contour scale space, which will provide an excellent corner description. Specifically, this research work is as follows:①The Laplacian differential operator is applied to the contour curves. According to the property that a corner is non-smooth in the curve, the corner response is defined as the 2-norm of Laplacian of the curve (referred to as the Laplacian corner response). These responses are able to locate the same corners as the popular curvature corner response. Therefore, the definition is consistent with previous.②The evolved contours is obtained by convolving the original contour with Gaussian function at different scales, which consist of the Gaussian scale space of contours. And the Laplacian corner reponse of the evolved contours is defined as the Laplacian of Gaussian (LoG) scale space of corners. Afterwards, the behavior of LoG corners is investigated in the form of analytical solution via single and double corner models.③The LoG corner detection algorithm is proposed on the basis of the behavior of LoG corners. In addition, the Difference of Gaussian (DoG) operator that is the approximation of LoG operator is used to construct the DoG corner detection algorithm. Eventually, both the LoG and DoG algorithm are proved to process more superior detection performance and stronger robustness against to noise than the classic CSS(Curvature Scale Space) algorithm.