| robust maximum likelihood estimation by sparse bundle adjustment using the l1 norm |
| Dai Zhijun; Zhang Fengjun; Wang Hongan
|
| 2012
|
| Conference Name | 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
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| Source | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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| Pages | 1672-1679
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| Conference Date | June 16, 2012 - June 21, 2012
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| Conference Place | Providence, RI, United states
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| Indexed Type | EI
; ISTP
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| ISSN | 1063-6919
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| ISBN | 9781467312264
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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
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| English Abstract | Sparse bundle adjustment is widely used in many computer vision applications. In this paper, we propose a method for performing bundle adjustments using the L1 norm. After linearizing the mapping function in bundle adjustment on its first order, the kernel step is to compute the L1 norm equations. Considering the sparsity of the Jacobian matrix in linearizing, we find two practical methods to solve the L1 norm equations. The first one is an interior-point method, which transfer the original problem to a problem of solving a sequence of L2 norm equations, and the second one is a decomposition method which uses the differentiability of linear programs and represents the optimal updating of parameters of 3D points by the updating variables of camera parameters. The experiments show that the method performs better for both synthetically generated and real data sets in the presence of outliers or Laplacian noise compared with the L2 norm bundle adjustment, and the method is efficient among the state of the art L1 minimization methods. © 2012 IEEE.; Sparse bundle adjustment is widely used in many computer vision applications. In this paper, we propose a method for performing bundle adjustments using the L1 norm. After linearizing the mapping function in bundle adjustment on its first order, the kernel step is to compute the L1 norm equations. Considering the sparsity of the Jacobian matrix in linearizing, we find two practical methods to solve the L1 norm equations. The first one is an interior-point method, which transfer the original problem to a problem of solving a sequence of L2 norm equations, and the second one is a decomposition method which uses the differentiability of linear programs and represents the optimal updating of parameters of 3D points by the updating variables of camera parameters. The experiments show that the method performs better for both synthetically generated and real data sets in the presence of outliers or Laplacian noise compared with the L2 norm bundle adjustment, and the method is efficient among the state of the art L1 minimization methods. © 2012 IEEE. |
| Keyword | Jacobian Matrices
Maximum Likelihood Estimation
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| Sponsorship | IEEE
|
| Subject | Computer Science
; Engineering
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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/311060/15789
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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,Zhang Fengjun,Wang Hongan. robust maximum likelihood estimation by sparse bundle adjustment using the l1 norm[C],2012:1672-1679.
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