Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields

doi:10.1109/TIP.2017.2658947

CORC > 上海财经大学 > 上海财经大学

	Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields
	Wang, Gang 1; Zhou, Qiangqiang 2; Chen, Yufei 2
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2017-04
卷号	26 期号:4 页码:1759-1769
关键词	Gaussian fields registration non-rigid transformation graph Laplacian regularization correspondence
ISSN号	1057-7149
DOI	10.1109/TIP.2017.2658947
英文摘要	Estimating transformations from degraded point sets is necessary for many computer vision and pattern recognition applications. In this paper, we propose a robust non-rigid point set registration method based on spatially constrained context-aware Gaussian fields. We first construct a context-aware representation (e.g., shape context) for assignment initialization. Then, we use a graph Laplacian regularized Gaussian fields to estimate the underlying transformation from the likely correspondences. On the one hand, the intrinsic manifold is considered and used to preserve the geometrical structure, and a priori knowledge of the point set is extracted. On the other hand, by using the deterministic annealing, the presented method is extended to a projected high-dimensional feature space, i.e., reproducing kernel Hilbert space through a kernel trick to solve the transformation, in which the local structure is propagated by the coarse-to-fine scaling strategy. In this way, the proposed method gradually recovers much more correct correspondences, and then estimates the transformation parameters accurately and robustly when facing degradations. Experimental results on 2D and 3D synthetic and real data (point sets) demonstrate that the proposed method reaches better performance than the state-of-the-art algorithms.
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000395902900018
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/1011]
专题	上海财经大学
通讯作者	Wang, Gang
作者单位	1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China; 2.Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China
推荐引用方式 GB/T 7714	Wang, Gang,Zhou, Qiangqiang,Chen, Yufei. Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(4):1759-1769.
APA	Wang, Gang,Zhou, Qiangqiang,&Chen, Yufei.(2017).Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(4),1759-1769.
MLA	Wang, Gang,et al."Robust Non-Rigid Point Set Registration Using Spatially Constrained Gaussian Fields".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.4(2017):1759-1769.