Piecewise cubic mapping functions for image registration
Pattern Recognition
An adaptive method for image registration
Pattern Recognition
Image warping by radial basis functions: applications to facial expressions
CVGIP: Graphical Models and Image Processing
The nature of statistical learning theory
The nature of statistical learning theory
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
An overview of statistical learning theory
IEEE Transactions on Neural Networks
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This paper describes a new approach to the determination of a mapping function from given coordinates of control points based on least square support vector machines (LS-SVM). An interesting property of this approach is that it constitutes a practical implementation of the structural risk minimization (SRM) principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the mean square error over control points. Computer simulation results indicate that this new approach can remove geometric deformation and adapt to correct the errors induced by inaccuracy location of control points.