提出新型高分辨率角点检测方法，有效解决邻近角点干扰问题
Second-order Gaussian directional derivative representations for image high-resolution corner detection
- 基于二阶高斯方向导数滤波，优化角点特征提取机制
- 在真实图像上实现更小定位误差与更强抗模糊鲁棒性
- 适合需要精准角点定位的图像匹配与三维重建任务
角点检测广泛应用于图像匹配和三维重建等计算机视觉任务。研究发现，张等人采用简单角点模型时存在理论缺陷，相邻角点的灰度信息会相互影响。为此，本文引入二阶高斯方向导数（SOGDD）滤波器，对两种典型高分辨率角点模型（即END型和L型）进行平滑处理，并分别推导其SOGDD表示，揭示了多个高分辨率角点特性，从而明确了如何选择高斯滤波尺度以准确获取图像中的强度变化信息，精确刻画邻近角点。此外，首次提出一种新的高分辨率角点检测方法，可准确识别邻近角点。实验表明，该方法在定位误差、抗图像模糊能力、图像匹配及三维重建性能上均优于现有先进方法。
原文摘要 · Abstract (English)
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.
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