Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A direct method for stereo correspondence based on singular value decomposition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
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This paper investigates a kernel spectral approach to the problem of point pattern matching. Our first contribution is to show how kernel principal components analysis can be effectively used for solving the point correspondence matching problem when the point-sets are subject to structural errors, i.e. they are of different size. Our second contribution is to show how label consistency constraints can be incorporated into the construction of the Gram matrices for solving the articulated point pattern matching problem. We compare our algorithm with earlier point matching approaches and provide experiments on both synthetic data and real world data.