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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 457495, 12 pages
Research Article

Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching

School of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China

Received 13 January 2015; Accepted 19 March 2015

Academic Editor: Dongrong Xu

Copyright © 2015 Kangho Paek et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.