Corner Detection Based on Multi-directional Gabor Filters with Multi-scales
This is an incremental improvement for computer vision tasks like 3D reconstruction, addressing limitations of existing Gabor-based methods in complex scenes.
The authors tackled the problem of corner detection in complex scenes by proposing a new method based on multi-directional Gabor filters with multi-scales, which outperformed twelve state-of-the-art methods and showed good potential for 3D reconstruction applications.
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.