Multisubject Non-rigid Registration of Brain MRI Using Intensity and Geometric Features
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Global registration of overlapping images using accumulative image features
Pattern Recognition Letters
Region-based image registration for mosaicking
International Journal of Computer Applications in Technology
International Journal of Computer Vision
Sparse Representations for Efficient Shape Matching
SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
Partially occluded object recognition
International Journal of Computer Applications in Technology
Efficient many-to-many feature matching under the l1 norm
Computer Vision and Image Understanding
Monomodal registration with adaptive parameter computing
International Journal of Computer Applications in Technology
A probabilistic model for correspondence problems using random walks with restart
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
A new algorithm of feature matching is proposed after balancing analysis of adjacency matrix of the matching model in a probabilistic framework. Considering all the interaction of the two feature point sets, a probabilistic model is established and solved using random walks with restart RWR. To reduce the influence of deformation, and increase the accuracy of feature matching algorithm, a balancing analysis to the adjacency matrix of RWR is taken. Then an efficient method for bidirectional balance is presented, which makes the relevance weight between each two correspondence candidates balanced. The approach considers not only all the correspondence candidates of the two feature point sets, but also the geometrical relation between each pair of candidates. It improves the discriminative and accuracy performance of matching. Compared with other state-of-the-art algorithms, the method is more robust to outliers and geometric deformation, and is accurate in terms of matching rate in various matching applications, such as object localisation.