A Maximum Likelihood Approach to Joint Groupwise Image Registration and Fusion by a Student-t Mixture Model

被引：0
|
作者：
Zhu, Hao [1 ]
Tang, Chunxia [1 ]
De Freitas, Allan [2 ]
Mihaylova, Lyudmila [3 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing, Peoples R China
[2] Univ Pretoria, Dept Elect Elect & Comp Engn, Pretoria, South Africa
[3] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield, S Yorkshire, England
来源：
2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
image registration; image fusion; Student-t mixture model; expectation maximization; MAXIMIZATION; PERFORMANCE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a Student-t mixture model (SMM) to approximate the joint intensity scatter plot (JISP) of the groupwise images. The problem of joint groupwise image registration and fusion is considered as a maximum likelihood (ML) formulation. The parameters of registration and fusion are estimated simultaneously by an expectation maximization (EM) algorithm. To evaluate the performance of the proposed method, experiments on several types of multimodal images are performed. Comprehensive experiments demonstrate that the proposed approach has better performance than other methods.
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页数：7
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