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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhu, Qingxiang Wang, Botao Xiong, Hongkai Luo, Chuanfei |
| Abstract | Variational methods are among the most accurate techniques of optical flow computation. $TV-L^{1}$ optical flow, which is based on $L^{1}-norm$ data fidelity term and total variation (TV) regularization term, preserves discontinuities in the flow field and also can deal with large displacements. However, the $TV-L^{1}$ optical flow method is inaccurate near edges and computationally intensive. In this paper, we proposed a technique, called Edge-based Image Decomposition (EID), to improve the accuracy in the edge areas and also accelerate the original $TV-L^{1}$ method. EID improves the performance by decomposing image into edge regions and flat regions, and also assigns computing power discriminatively. We evaluated our algorithm on Middlebury datasets and proved that by applying EID, 30% of run-time can be saved with no loss in accuracy, and with same run-time, 7% of accuracy can be promoted. In addition, we implemented our EID-enhanced $TV-L^{1}$ optical flow algorithm on mobile phone with Android operating system. Our application calculates the optical flow field between two images and can be used to generate the disparity map and reconstruct 3D scenes. |
| Starting Page | 144 |
| Ending Page | 151 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450310963 |
| DOI | 10.1145/2107596.2107614 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-12-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Total variation Scale-space approach Edge-based image decomposition Optical flow |
| Content Type | Text |
| Resource Type | Article |
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