Second-order Gaussian directional derivative representations for image high-resolution corner detection
This work addresses a specific issue in computer vision for tasks like image matching and 3D reconstruction, but it appears incremental as it builds on existing corner detection methods by refining models to handle adjacent corners.
The authors tackled the problem of detecting adjacent corner points in images by proposing a second-order Gaussian directional derivative filter to smooth high-resolution angle models, which led to a new corner detection method that outperformed state-of-the-art methods in localization error, robustness to blur, image matching, and 3D reconstruction.
Corner detection is widely used in various computer vision tasks, such as image matching and 3D reconstruction. Our research indicates that there are theoretical flaws in Zhang et al.'s use of a simple corner model to obtain a series of corner characteristics, as the grayscale information of two adjacent corners can affect each other. In order to address the above issues, a second-order Gaussian directional derivative (SOGDD) filter is used in this work to smooth two typical high-resolution angle models (i.e. END-type and L-type models). Then, the SOGDD representations of these two corner models were derived separately, and many characteristics of high-resolution corners were discovered, which enabled us to demonstrate how to select Gaussian filtering scales to obtain intensity variation information from images, accurately depicting adjacent corners. In addition, a new high-resolution corner detection method for images has been proposed for the first time, which can accurately detect adjacent corner points. The experimental results have verified that the proposed method outperforms state-of-the-art methods in terms of localization error, robustness to image blur transformation, image matching, and 3D reconstruction.