| a novel fast method for l∞ problems in multiview geometry |
| Dai Zhijun; Wu Yihong; Zhang Fengjun; Wang Hongan
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| 2012
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| Conference Name | 12th European Conference on Computer Vision, ECCV 2012
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| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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| Pages | 116-129
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| Conference Date | October 7, 2012 - October 13, 2012
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| Conference Place | Florence, Italy
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| Indexed Type | EI
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| ISSN | 0302-9743
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| ISBN | 9783642337147
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| Department | (1) Intelligence Engineering Lab. Institute of Software Chinese Academy of Sciences China; (2) State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences China; (3) National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China
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| English Abstract | Optimization using the L∞ norm is an increasingly important area in multiview geometry. Previous work has shown that globally optimal solutions can be computed reliably using the formulation of generalized fractional programming, in which algorithms solve a sequence of convex problems independently to approximate the optimal L∞ norm error. We found the sequence of convex problems are highly related and we propose a method to derive a Newton-like step from any given point. In our method, the feasible region of the current involved convex problem is contracted gradually along with the Newton-like steps, and the updated point locates on the boundary of the new feasible region. We propose an effective strategy to make the boundary point become an interior point through one dimension augmentation and relaxation. Results are presented and compared to the state of the art algorithms on simulated and real data for some multiview geometry problems with improved performance on both runtime and Newton-like iterations. © 2012 Springer-Verlag.; Optimization using the L∞ norm is an increasingly important area in multiview geometry. Previous work has shown that globally optimal solutions can be computed reliably using the formulation of generalized fractional programming, in which algorithms solve a sequence of convex problems independently to approximate the optimal L∞ norm error. We found the sequence of convex problems are highly related and we propose a method to derive a Newton-like step from any given point. In our method, the feasible region of the current involved convex problem is contracted gradually along with the Newton-like steps, and the updated point locates on the boundary of the new feasible region. We propose an effective strategy to make the boundary point become an interior point through one dimension augmentation and relaxation. Results are presented and compared to the state of the art algorithms on simulated and real data for some multiview geometry problems with improved performance on both runtime and Newton-like iterations. © 2012 Springer-Verlag. |
| Keyword | Algorithms
Geometry
Optimization
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| Sponsorship | Google; National Robotics Engineering Center (NREC); Adobe; Microsoft Research; Mitsubishi Electric
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| Language | 英语
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| Content Type | 会议论文
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| URI | http://ir.iscas.ac.cn/handle/311060/15826
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| Collection | 中国科学院软件研究所
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Recommended Citation
GB/T 7714 |
Dai Zhijun,Wu Yihong,Zhang Fengjun,et al. a novel fast method for l∞ problems in multiview geometry[C],2012:116-129.
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