Multi-viewpoint image mosaicing algorithm for roughly planar scenes

被引：0
作者：
Xu, Yuhua [1 ,2 ]
Gong, Wenquan [2 ]
Su, Ang [1 ]
Zhang, Yueqiang [1 ]
Zhang, Xiaohu [1 ]
机构：
[1] College of Aerospace Science and Engineering, National University of Defense Technology
[2] The Army of 96634
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2014年 / 36卷 / 02期
关键词：
3D reconstruction; Image mosaicing; Sparse global adjustment; Unmanned aerial vehicle (UAV);
D O I：
10.11887/j.cn.201402025
中图分类号：
学科分类号：
摘要：
Aiming at applications of small unmanned aerial vehicle (UAV) in earth observations, this research explores the multi-viewpoint image mosaicing problems for roughly planar scenes. When coarse camera poses are known, a method for integrating camera poses and feature correspondences is proposed, in which direct sparse Cholesky factorization algorithm is used to solve the global optimization problems of mosaicing. Global distortions do not exist in the obtained mosaics and local mosaic errors are suppressed effectively. When the camera poses are unknown, a structure-from-motion (SFM) system is used to recover the cameras poses and sparse structure of the scene firstly. Then, the sparse global adjustment is used to refine the transformations. The proposed algorithm is validated using sand table images and real aerial images.
引用
收藏
页码：148 / 155
页数：7
相关论文
共 22 条
  • [1] Lin Z., Li D., Xu Y., General review on the new progress of earth observations, Science of Surveying and Mapping, 36, 4, pp. 5-8, (2011)
  • [2] Heinze N., Esswein M., Kruger W., Et al., Automatic image exploitation system for small UAVs, Proceedings of SPIE Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, (2008)
  • [3] Se S., Firoozfam P., Goldstein N., Et al., Automated UAV-based mapping for airborne reconnaissance and video exploitation, Proceedings of SPIE Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, (2009)
  • [4] Bryson M., Reid A., Ramos F., Et al., Airborne vision-based mapping and classification of large farmland environments, Journal of Field Robotics, 27, 5, pp. 632-655, (2010)
  • [5] Yin P., Yin Q., Chen X., Et al., Unmanned aerial vehicle aerial remote sensing techniques and its application on post earthquake disaster investigation, Laser & Optoelectronics Progress, 47, 11, pp. 130-134, (2010)
  • [6] Yahyanejad S., Wischounig-Strucl D., Quaritsch M., Et al., Incremental mosaicing of images from autonomous small-scale UAVs, Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 329-336, (2010)
  • [7] Brown M., Lowe D.G., Automatic panoramic image stitching using invariant features, International Journal of Computer Vision, 74, 1, pp. 59-73, (2007)
  • [8] Harris C., Stephens M., A combined corner and edge detector, Proceedings of Fourth Alvey Vision Conference, pp. 147-151, (1988)
  • [9] Lowe D.G., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2, pp. 91-110, (2004)
  • [10] Bay H., Tuytelaars T., van Gool L., SURF: Speeded up robust features, European Conference on Computer Vision, pp. 404-417, (2006)
123