Indexed by:
Abstract:
The emergent one-dimensional (1D) calibration is very suitable for multi-camera calibration. However its accuracy is not satisfactory. Conventional optimal algorithms, such as bundle adjustment, do not perform well for the non-convex optimization of 1D calibration. In this paper, a practical optimal algorithm for camera calibration with 1D objects using branch and bound framework is presented. To obtain the optimal solution which can provide Ε-optimality, tight convex relaxations of the objective functions are constructed and minimized in a branch and bound optimization framework. Experiments prove the validity of the proposed method. © 2011, Springer-Verlag Berlin Heidelberg.
Keyword:
Reprint Author's Address:
Email:
Source :
ISSN: 0302-9743
Year: 2011
Volume: 6761 LNCS
Page: 660-669
Language: English
Cited Count:
SCOPUS Cited Count: 11
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 0
Affiliated Colleges: