Next Article in Journal
Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network
Next Article in Special Issue
Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot’s Workload Condition
Previous Article in Journal
Development of a Simple Assay Method for Adenosine Deaminase via Enzymatic Formation of an Inosine-Tb3+ Complex
Previous Article in Special Issue
A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles
Open AccessArticle

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

1
Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, and Automotive Electronics and Embedded System Engineering Research Center, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2729; https://doi.org/10.3390/s19122729
Received: 24 April 2019 / Revised: 10 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
(This article belongs to the Collection Multi-Sensor Information Fusion)
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. View Full-Text
Keywords: image registration; non-rigid feature matching; local structure descriptor; Gaussian mixture model image registration; non-rigid feature matching; local structure descriptor; Gaussian mixture model
Show Figures

Figure 1

MDPI and ACS Style

Zhu, H.; Zou, K.; Li, Y.; Cen, M.; Mihaylova, L. Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving. Sensors 2019, 19, 2729.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop