Ensemble image registration by a spatially constrained clustering approach

Author:

Zhu Hao12,Shi Qiqun2,Li Yongfu2,Wu Qiuxuan1

Affiliation:

1. Department of Automation, Hangzhou Dianzi University, Hangzhou, China

2. Department of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China

Abstract

In this article, a novel spatially constrained clustering approach is proposed for ensemble image registration. We use a spatially constrained Gaussian mixture model, which is based on a joint Gaussian mixture model and Markov random field, to model the joint intensity scatter plot of the unregistered images. The spatially constrained Gaussian mixture model has the capability of performing the correlation among neighboring observations. A cost function of reducing the dispersion in the joint intensity scatter plot is proposed using the spatially constrained Gaussian mixture model to simultaneously register a group of images. We derive an expectation maximization algorithm for the proposed model. Computer simulations demonstrate the effectiveness of the proposed method.

Publisher

SAGE Publications

Subject

Artificial Intelligence,Computer Science Applications,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Maximum Likelihood Approach to Joint Groupwise Image Registration and Fusion by a Student-$t$ Mixture Model;2019 22th International Conference on Information Fusion (FUSION);2019-07

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