14 December 2015 UAV multiple image dense matching based on self-adaptive patch
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 981305 (2015) https://doi.org/10.1117/12.2203581
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
This article using some state-of-art multi-view dense matching methods for reference, proposes an UAV multiple image dense matching algorithm base on Self-Adaptive patch (UAV-AP) in view of the specialty of UAV images. The main idea of matching propagating based on Self-Adaptive patch is to build patches centered by seed points which are already matched. The extent and figure of the patches can adapt to the terrain relief automatically: when the surface is smooth, the extent of the patch would become bigger to cover the whole smooth terrain; while the terrain is very rough, the extent of the patch would become smaller to describe the details of the surface. With this approach, the UAV image sequences and the given or previously triangulated orientation elements are taken as inputs. The main processing procedures are as follows: (1) multi-view initial feature matching, (2) matching propagating based on Self-Adaptive patch, (3) filtering the erroneous matching points. Finally, the algorithm outputs a dense colored point cloud. Experiments indicate that this method surpassed the existing related algorithm in efficiency and the matching precision is also quite ideal.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Zhu, Jin Zhu, Yazhou Ding, Yazhou Ding, Xiongwu Xiao, Xiongwu Xiao, Bingxuan Guo, Bingxuan Guo, Deren Li, Deren Li, Nan Yang, Nan Yang, Weilong Zhang, Weilong Zhang, Xiangxiang Huang, Xiangxiang Huang, Linhui Li, Linhui Li, Zhe Peng, Zhe Peng, Fei Pan, Fei Pan, } "UAV multiple image dense matching based on self-adaptive patch", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981305 (14 December 2015); doi: 10.1117/12.2203581; https://doi.org/10.1117/12.2203581
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