云计算下无人机采集无序位置图像快速拼接
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摘要
研究云计算环境下无人机采集无序图像快速拼接问题。无人机采集图像中,飞行速度、位置都呈现不可控性,采集的大量图像也呈现无序状态,存在重复采集、缺失采集的情况,在传统的云计算环境下,拼接图像需要大量重复认证、对比,匹配过程效率很低。为此提出了一种基于哈密顿通路模型的云计算下无人机采集无序图像快速拼接方法。对无人机采集的无序图像进行空间位置几何畸变补偿,建立哈密顿通路模型,将云计算下无人机采集无序图像拼接问题转换为哈密顿通路模型求解问题,利用哈密顿通路求解的快速性实现云计算下无人机采集无序图像快速拼接方法。实验结果表明,利用改进算法进行云计算下无人机采集无序图像快速拼接,能够提高图像的匹配效率,缩短拼接时间。
In this paper,the problem of quickly stitching of disorderly images acquired by unmanned aerial vehicle( UAV) under cloud computing environment was researched. W proposed a method for quickly stitching of disorderly images which are acquired by unmanned aerial vehicle( UAV) under cloud computing environment based on the Hamiltonian path model. Firstly,the disorderly images acquired by UAV were conducted to make geometric distortion compensation of spatial position and construct the Hamiltonian path model. Then,the disorderly image stitching problem was converted into the problem of Hamiltonian path model under the cloud computing environment. Meanwhile, by taking the advantage of the fast solving features of Hamiltonian path,the quickly stitching of disorderly images acquired by UAV was ultimately accomplished under cloud computing environment. Experimental results show that the improved algorithm for quickly stitching of disorderly images acquired by UAV under cloud computing environment can enhance the efficiency of image matching and shorten stitching time.
In this paper,the problem of quickly stitching of disorderly images acquired by unmanned aerial vehicle( UAV) under cloud computing environment was researched. W proposed a method for quickly stitching of disorderly images which are acquired by unmanned aerial vehicle( UAV) under cloud computing environment based on the Hamiltonian path model. Firstly,the disorderly images acquired by UAV were conducted to make geometric distortion compensation of spatial position and construct the Hamiltonian path model. Then,the disorderly image stitching problem was converted into the problem of Hamiltonian path model under the cloud computing environment. Meanwhile, by taking the advantage of the fast solving features of Hamiltonian path,the quickly stitching of disorderly images acquired by UAV was ultimately accomplished under cloud computing environment. Experimental results show that the improved algorithm for quickly stitching of disorderly images acquired by UAV under cloud computing environment can enhance the efficiency of image matching and shorten stitching time.
引文
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[2]L Olsen,et al.Sketch-based modeling a survey[J].Computers&Craphics,2009,33(1):85-103.
[3]W S Lee,N Magnenat-Thalmann.Fast head modeling for animation[J].Image and Vision Computing,2000,18(4):355-364.
[4]Zhang Qian,Liu Zhengkai,Pang Yanwei.Automatic registration of aerophotos based on SUSAN operator[J].Acta Geodaetica et Cartographica Sinica,2003,32(3):245-250.
[5]周文俊,曹健.基于预测及蚁群算法的云计算资源调度策略[J].计算机仿真,2012-9:239-242.
[6]於时才,吕艳琼.一种基于小波变换的图像融合新算法[J].计算机应用与研究,2009,26(1):390-392.
[7]李寒,等.基于特征点的全自动无缝图像拼接方法[J].计算机工程与设计,2007,28(9):2083-2085.
[8]王琰等.基于熵的柱面全景图拼接算法及其鲁棒性分析[J].小型微型计算机系统,2007,28(1):148-150.
[9]X H Wang,W P Fu.Optimized sift image matching algorithm[J].Automation and Logistics,2008:843-847.
[10]殷润民,李伯虎,柴旭东.自适应柱状全景图拼接[J].中国图形图像学报,2008,13(6):1191-1196.