Second-order Gaussian directional derivative representations for image high-resolution corner detection

2026-01-13

AI Summary

This study addresses the challenge of inaccurate localization in existing corner detection methods when handling adjacent high-resolution corners due to grayscale interference. To overcome this limitation, the authors propose a novel detection approach based on second-order Gaussian directional derivatives (SOGDD). By constructing END-type and L-type high-resolution corner models, they derive their SOGDD representations, revealing the intrinsic characteristics of corner intensity variation. Leveraging these insights, a Gaussian scale selection criterion is established to effectively suppress mutual interference among neighboring corners. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art techniques in terms of localization accuracy, robustness to blur, and performance in downstream tasks such as image matching and 3D reconstruction, achieving, for the first time, highly accurate detection of densely packed high-resolution corners.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Search and Optimization: Local Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• corner detection
• high-resolution corners
• adjacent corners
• grayscale interference
• localization accuracy

Innovation

• second-order Gaussian directional derivative
• high-resolution corner detection
• adjacent corner modeling
• Gaussian scale selection
• corner representation