Gabor wavelet is an essential tool for image analysis and computer vision tasks. Local structure tensors with multiple scales are widely used in local feature extraction. Our research indicates that the current corner detection method based on Gabor wavelets can not effectively apply to complex scenes. In this work, the capability of the Gabor function to discriminate the intensity changes of step edges, L-shaped corners, Y-shaped or T-shaped corners, X-shaped corners, and star-shaped corners are investigated. The properties of Gabor wavelets to suppress affine image transformation are investigated and obtained. Many properties for edges and corners were discovered, which prompted us to propose a new corner extraction method. To fully use the structural information from the tuned Gabor filters, a novel multi-directional structure tensor is constructed for corner detection, and a multi-scale corner measurement function is proposed to remove false candidate corners. Furthermore, we compare the proposed method with twelve current state-of-the-art methods, which exhibit optimal performance and practical application to 3D reconstruction with good application potential.

翻译：Gabor小波是图像分析与计算机视觉任务的重要工具。多尺度局部结构张量广泛应用于局部特征提取。研究表明，当前基于Gabor小波的角点检测方法难以有效适用于复杂场景。本文研究了Gabor函数对阶跃边缘、L形角点、Y形或T形角点、X形角点及星形角点强度变化的分辨能力，探究并获得了Gabor小波抑制仿射图像变换的特性。通过发现边缘与角点的多种性质，我们提出了一种新的角点提取方法。为充分利用调谐Gabor滤波器的结构信息，构建了用于角点检测的新型多方向结构张量，并提出了多尺度角点度量函数以去除虚假候选角点。此外，将所提方法与十二种当前最优方法进行了比较，结果表明该方法在三维重建中展现出优异性能与良好应用潜力。