ECFRNet: Effective corner feature representations network for image corner detection

Abstract

Interest points (corners and blobs) play an important role in computer vision tasks such as image matching, image retrieval, and 3D reconstruction. Existing deep learning based interest point detection methods mainly focus on the interest point detection with high repeatability under image affine transformations while neglecting the importance of the characteristics of interest points. This will affect the detection and localization accuracy of interest points. In this paper, we design an effective corner feature representations network based on the characteristics of corners. The designed network has the ability to effectively learn corner feature information from images. A novel loss function is proposed to minimize the localization error between the corner positions of the original image block and the transformed image blocks. Furthermore, a novel corner detection architecture is proposed. The criteria on detection accuracy, localization accuracy, average repeatability, region repeatability, and image matching score are used to evaluate the proposed method against fourteen state-of-the-art methods. The experimental results show that the proposed performs significantly better than the state-of-the-arts.