Homography estimation in omnidirectional vision under the L ∞-norm

Liwei Zhang; Youfu Li; Jianwei Zhang; Ying Hu

doi:10.1109/ROBIO.2010.5723546

Homography estimation in omnidirectional vision under the L ∞-norm

Liwei Zhang
, Youfu Li^*
, Jianwei Zhang
, Ying Hu

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L_∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds of frameworks in this paper. This approach provides a theoretical guarantee of global optimality. A robot with this algorithm, which provides global optimality and a wide field of view demonstrated by good performance in experiments for synthetic and real data, has a more exact location and 3D reconstruction ability, which cannot be provided by traditional homography estimate method under traditional vision system. © 2010 IEEE.

Original language	English
Title of host publication	2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
Pages	1468-1473
DOIs	https://doi.org/10.1109/ROBIO.2010.5723546
Publication status	Published - 2010
Event	2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010 - Tianjin, China Duration: 14 Dec 2010 → 18 Dec 2010

Conference

Conference	2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010
Place	China
City	Tianjin
Period	14/12/10 → 18/12/10

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10.1109/ROBIO.2010.5723546

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@inproceedings{bfde0a1c882a4beca6bfc0df5f779b5b,

title = "Homography estimation in omnidirectional vision under the L ∞-norm",

abstract = "Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds of frameworks in this paper. This approach provides a theoretical guarantee of global optimality. A robot with this algorithm, which provides global optimality and a wide field of view demonstrated by good performance in experiments for synthetic and real data, has a more exact location and 3D reconstruction ability, which cannot be provided by traditional homography estimate method under traditional vision system. {\textcopyright} 2010 IEEE.",

author = "Liwei Zhang and Youfu Li and Jianwei Zhang and Ying Hu",

year = "2010",

doi = "10.1109/ROBIO.2010.5723546",

language = "English",

isbn = "9781424493173",

pages = "1468--1473",

booktitle = "2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010",

note = "2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010 ; Conference date: 14-12-2010 Through 18-12-2010",

}

Homography estimation in omnidirectional vision under the L ∞-norm. / Zhang, Liwei; Li, Youfu; Zhang, Jianwei et al.
2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010. 2010. p. 1468-1473 5723546.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Homography estimation in omnidirectional vision under the L ∞-norm

AU - Zhang, Liwei

AU - Li, Youfu

AU - Zhang, Jianwei

AU - Hu, Ying

PY - 2010

Y1 - 2010

N2 - Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds of frameworks in this paper. This approach provides a theoretical guarantee of global optimality. A robot with this algorithm, which provides global optimality and a wide field of view demonstrated by good performance in experiments for synthetic and real data, has a more exact location and 3D reconstruction ability, which cannot be provided by traditional homography estimate method under traditional vision system. © 2010 IEEE.

AB - Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds of frameworks in this paper. This approach provides a theoretical guarantee of global optimality. A robot with this algorithm, which provides global optimality and a wide field of view demonstrated by good performance in experiments for synthetic and real data, has a more exact location and 3D reconstruction ability, which cannot be provided by traditional homography estimate method under traditional vision system. © 2010 IEEE.

UR - https://www.scopus.com/pages/publications/79952937506

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79952937506&origin=recordpage

U2 - 10.1109/ROBIO.2010.5723546

DO - 10.1109/ROBIO.2010.5723546

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424493173

SP - 1468

EP - 1473

BT - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010

T2 - 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010

Y2 - 14 December 2010 through 18 December 2010

ER -

Homography estimation in omnidirectional vision under the L ∞-norm

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