Yang Zhanlong, Shen Dinggang, Yap Pew-Thian
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, United States of America.
PLoS One. 2017 Mar 15;12(3):e0173627. doi: 10.1371/journal.pone.0173627. eCollection 2017.
In this paper, we present a novel image mosaicking method that is based on Speeded-Up Robust Features (SURF) of line segments, aiming to achieve robustness to incident scaling, rotation, change in illumination, and significant affine distortion between images in a panoramic series. Our method involves 1) using a SURF detection operator to locate feature points; 2) rough matching using SURF features of directed line segments constructed via the feature points; and 3) eliminating incorrectly matched pairs using RANSAC (RANdom SAmple Consensus). Experimental results confirm that our method results in high-quality panoramic mosaics that are superior to state-of-the-art methods.
在本文中,我们提出了一种基于线段加速稳健特征(SURF)的新型图像拼接方法,旨在实现对全景序列中图像间的入射缩放、旋转、光照变化以及显著仿射失真的鲁棒性。我们的方法包括:1)使用SURF检测算子定位特征点;2)利用通过特征点构建的有向线段的SURF特征进行粗匹配;3)使用随机抽样一致性算法(RANSAC)消除错误匹配的对。实验结果证实,我们的方法能够生成优于现有方法的高质量全景拼接图。