Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving.

Hao Zhu,Lyudmila Mihaylova,Ming Cen,Ke Zou,Yongfu Li

doi:10.3390/s19122729

Abstract

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

Highlights

Image registration is a fundamental task in many fields, such as computer vision, robotics, medical image processing, and remote sensing [1,2,3,4]
We propose a novel Gaussian mixture model (GMM)-based non-rigid point set registration method to perform image feature matching with structure constraints
The feature points of one image are represented by GMM centroids, and the feature points of the other image are considered as data points

Summary

Introduction

Image registration is a fundamental task in many fields, such as computer vision, robotics, medical image processing, and remote sensing [1,2,3,4]. The main purpose of image registration is to align two or more images of the same scene taken from different viewpoints, at different times, and/or by different sensors. Many algorithms have been developed for image registration. It can be roughly divided into area-based and feature-based methods. Area-based methods match image intensity values directly. They mainly include the cross-correlation (CC) methods, the Fourier methods, and mutual information (MI) methods. The similarity of window pairs from two images are computed and the maximum is considered as a correspondence

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