Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching. (5th May 2015)

• Record Type: Journal Article
• Authors: Paek, Kangho; Yao, Min; Liu, Zhongwei; Kim, Hun
• Other Names: Xu Dongrong Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2015(2015)
• Publication Date: 2015-05-05
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2015/457495
• Languages: English
• ISSNs: 1687-5265

Abstract

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.