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Ultrarobust support vector registration
Yin, Lei1; Yu, Chong2; Wang, Yuyi3,4; Zou, Bin1; Tang, Yuan Yan5
2021-06-01
Source PublicationApplied Intelligence
ISSN0924-669X
Volume51Issue:6Pages:3664-3683
Abstract

An iterativeframework based on finding point correspondences and estimating the transformation function is widely adopted for nonrigid point set registration. However, correspondences established based on feature descriptors are likely to be inaccurate. In this paper, we propose a novel transformation model that can learn from such correspondences. The model is built by means of weighted support vector (SV) regression with a quadratic ε-insensitive loss and manifold regularization. The loss is insensitive to noise, and the regularization forces the transformation function to preserve the intrinsic geometry of the input data. To assess the confidences of correspondences, we introduce a probabilistic model that is solved using the expectation maximization (EM) algorithm. Then, we input the confidences into the transformation model as instance weights to guide model training. We use the coordinate descent method to solve the transformation model in a reproducing kernel Hilbert space and accelerate its speed by means of sparse approximation. Extensive experiments show that our approach is efficient and outperforms other state-of-the-art methods.

KeywordCoordinate Descent Method Em Algorithm Manifold Regularization Point Set Registration Weighted Sv Regression
DOI10.1007/s10489-020-01967-y
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000590258800004
Scopus ID2-s2.0-85096120344
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZou, Bin
Affiliation1.Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan, 430062, China
2.Mornengchen Intelligent Technology Co. Ltd., Shanghai, 200090, China
3.Department Electrical Engineering, ETH Zurich, Zurich, 358092, Switzerland
4.Theory Lab, Huawei 2012 Lab, Shanghai, 201206, China
5.Faculty of Science and Technology, University of Macau, Macau, 999078, China
Recommended Citation
GB/T 7714
Yin, Lei,Yu, Chong,Wang, Yuyi,et al. Ultrarobust support vector registration[J]. Applied Intelligence,2021,51(6):3664-3683.
APA Yin, Lei,Yu, Chong,Wang, Yuyi,Zou, Bin,&Tang, Yuan Yan.(2021).Ultrarobust support vector registration.Applied Intelligence,51(6),3664-3683.
MLA Yin, Lei,et al."Ultrarobust support vector registration".Applied Intelligence 51.6(2021):3664-3683.
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