title: Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching.
abstract: Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching.
authors.affiliation: School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China.
authors.forename: Kangho
authors.initials: K
authors.lastname: Paek
authors.affiliation: School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China.
authors.forename: Min
authors.identifier: 0000-0002-2966-6928
authors.initials: M
authors.lastname: Yao
authors.affiliation: School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China.
authors.forename: Zhongwei
authors.initials: Z
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China.
authors.forename: Hun
authors.initials: H
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity; D012984:Software