ArticleSensors (Basel, Switzerland)2019
Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Multi-Angle Optical Image Automatic Registration by Combining Point and Line Features.Sensors (Basel, Switzerland) · 2022Article
- Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model.Frontiers in neuroscience · 2022Article
- Adaptive and Efficient Mixture-Based Representation for Range Data.Sensors (Basel, Switzerland) · 2020Article
- A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences.Sensors (Basel, Switzerland) · 2020Article
- Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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