Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Canonical representations for the geometries of multiple projective views
Computer Vision and Image Understanding
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Maximizing the Predictivity of Smooth Deformable Image Warps through Cross-Validation
Journal of Mathematical Imaging and Vision
A comparative study of model selection criteria for computer vision applications
Image and Vision Computing
Generalized Thin-Plate Spline Warps
International Journal of Computer Vision
Journal of Medical Systems
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The prediction sum of squares is a useful statistic for comparing different models. It is based on the principle of leave-one-out or ordinary cross-validation, whereby every measurement is considered in turn as a test set, for the model parameters trained on all but the held out measurement. As for linear least squares problems, there is a simple well-known non-iterative formula to compute the prediction sum of squares without having to refit the model as many times as the number of measurements. We extend this formula to cases where the problem has multiple parameter or measurement sets.We report experimental results on the fitting of a warp between two images, for which the number of deformation centres is automatically selected, based on one of the proposed non-iterative formulae.