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RASL：线性相关图像的稀疏和低秩分解的鲁棒对齐算法RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images |
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| 课程网址: | http://videolectures.net/cvpr2010_wright_rasl/ |
| 主讲教师: | John Wright |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2010-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of 1 -norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions. |
| 关 键 词: | 线性相关; 图像域变换; 凸程序 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-13：lxf |
| 阅读次数: | 121 |