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| 作者 | Dai Zhijun
; Zhang Fengjun
; Wang Hongan
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| 出版日期 | 2012
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| 会议名称 | 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
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| 会议日期 | June 16, 2012 - June 21, 2012
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| 会议地点 | Providence, RI, United states
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| 关键词 | Jacobian matrices
Maximum likelihood estimation
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| 页码 | 1672-1679
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| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI
; ISTP
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| 会议主办者 | IEEE
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| 会议录 | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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| 学科主题 | Computer Science
; Engineering
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| 语种 | 英语
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| ISSN号 | 1063-6919
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| ISBN号 | 9781467312264
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| 源URL | [http://ir.iscas.ac.cn/handle/311060/15789]  |
| 专题 | 软件研究所_软件所图书馆_会议论文
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推荐引用方式
GB/T 7714 |
Dai Zhijun,Zhang Fengjun,Wang Hongan. robust maximum likelihood estimation by sparse bundle adjustment using the l1 norm[C]. 见:2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012. Providence, RI, United states. June 16, 2012 - June 21, 2012.
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