A total variation based nonrigid image registration by combining parametric and non-parametric transformation models
文献类型：期刊论文
| 作者 | Hu, Wenrui ; Xie, Yuan; Li, Lin; Zhang, Wensheng
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2014-11-20 |
| 卷号 | 144页码:222-237 |
| 关键词 | Nonrigid registration Free-form deformation Non-parametric transformation Total variation Split Bregman iteration |
| 英文摘要 | To overcome the conflict between the global robustness and the local accuracy of dense nonrigid image registration, we propose a union registration approach by combining parametric and non-parametric transformation models. On one hand, to guarantee the robustness, we constrain the displacement field phi using a mapping difference metric between the B-spline parametric space psi and the non-parametric transformation space (Phi. On the other hand, to correct the densely and highly localized geometrical distortions, we introduce a total variation (TV) regularization term for the displacement field phi. Accounting for the effect of spatially varying intensity distortions, the residual complexity (RC) is used as the similarity metric. Moreover, to solve the proposed union nonrigid registration, which is a composite convex optimization problem by the smooth l(2) term and the non-smooth l(2) term (TV), we design a two-stage algorithm using split Bregman iteration. Experiments with both synthetic and real images from different domains illustrate that this approach can capture the local details of transformation accurately and effectively while being robust to the spatially varying intensity distortions. (C) 2014 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | FREE-FORM DEFORMATIONS ; OPTICAL-FLOW ; ALGORITHMS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000341677800022 |
| 公开日期 | 2015-09-22 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8069]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Wenrui,Xie, Yuan,Li, Lin,et al. A total variation based nonrigid image registration by combining parametric and non-parametric transformation models[J]. NEUROCOMPUTING,2014,144:222-237. |
| APA | Hu, Wenrui,Xie, Yuan,Li, Lin,&Zhang, Wensheng.(2014).A total variation based nonrigid image registration by combining parametric and non-parametric transformation models.NEUROCOMPUTING,144,222-237. |
| MLA | Hu, Wenrui,et al."A total variation based nonrigid image registration by combining parametric and non-parametric transformation models".NEUROCOMPUTING 144(2014):222-237. |
入库方式： OAI收割
来源：自动化研究所
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