Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model

doi:10.1016/j.ins.2016.08.041

CORC > 上海财经大学 > 上海财经大学

	Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model
	Wang, Gang 1,2; Wang, Zhicheng 2; Chen, Yufei 2; Zhou, Qiangqiang 2; Zhao, Weidong 2
刊名	INFORMATION SCIENCES
	2016-12
卷号	372 页码:492-504
关键词	Retinal image registration Point set registration Mixture model Kernel method
ISSN号	0020-0255
DOI	10.1016/j.ins.2016.08.041
英文摘要	In order to address the problem of retinal image registration, this paper proposes and analyzes a novel and general matching algorithm called Multi-Attribute-Driven Regularized Mixture Model (MAD-RMM). Mismatches removal can play a key role in image registration, which refers to establish reliable matches between two point sets. Here the presented approach starts from multi-feature attributes which are used to guide the feature matching to identify inliers (correct matches) from outliers (incorrect matches), and then estimates the spatial transformation, In this paper, motivated by the problem of feature matching that the initial correspondence is always contaminated by outliers, thereby we formulate this issue as a probability deformable mixture model which consists of Gaussian components for inliers and uniform components for outliers. Moreover, the algorithm takes full advantage of using multiple attributes for better general matching performance. Here we are assuming all inliers are mapped into a high-dimensional feature space, namely reproducing kernel Hilbert space (RKHS), and the closed-form solution to the mapping function is given by the representation theorem with L-2 norm regularization under the Expectation Maximization (EM) algorithm. Finally, we evaluate the performance of the algorithm by applying it to retinal image registration on several datasets, where experimental results demonstrate that the MAD-RMM outperforms current state-of-the-art methods and shows the robustness to outliers on rear retinal images. (C) 2016 Elsevier Inc. All rights reserved.
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000384864300030
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/1168]
专题	上海财经大学
作者单位	1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China; 2.Tongji Univ, Coll Elect & Informat Engn, CAD Res Ctr, Shanghai 201804, Peoples R China
推荐引用方式 GB/T 7714	Wang, Gang,Wang, Zhicheng,Chen, Yufei,et al. Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model[J]. INFORMATION SCIENCES,2016,372:492-504.
APA	Wang, Gang,Wang, Zhicheng,Chen, Yufei,Zhou, Qiangqiang,&Zhao, Weidong.(2016).Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model.INFORMATION SCIENCES,372,492-504.
MLA	Wang, Gang,et al."Removing mismatches for retinal image registration via multi-attribute-driven regularized mixture model".INFORMATION SCIENCES 372(2016):492-504.