Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Regularization theory and neural networks architectures
Neural Computation
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Feature Registration Framework Using Mixture Models
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Correspondence Matching with Modal Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature point matching using a hermitian property matrix
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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This paper presents a novel algorithm of stereo correspondence by using Laplacian spectra of graphs. Firstly, according to the feature points of two images to be matched, a Laplacian matrix with Gaussian-weighted distance is defined and a closed-form solution is given in terms of the matching matrix constructed on the vectors of eigenspace of the Laplacian matrix. Secondly, we introduce a new method to judge correspondences by using doubly stochastic matrix. Thirdly, in order to render our method robust, we describe an approach to embedding the Laplacian spectral method within the framework of iterative correspondence and transformation estimation. Experimental results show the feasibility and comparatively high accuracy of our methods.