Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Image warping by radial basis functions: applications to facial expressions
CVGIP: Graphical Models and Image Processing
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Warping is one of the key areas of image analysis but there has been no understanding of the effects of different non-linear deformations in literature. This paper addresses the problem of the distortion effect produced by different types of non-linear deformation strategies on textured images. The images are modelled by a Gaussian random field. We first give various examples to illustrate that the model generates realistic images. We consider two types of deformations-a deterministic deformation and a landmark based deformation. The latter includes various radial basis type deformations including the thin-plate splines based deformation. The effects of deformations are assessed through Kullback-Leibler divergence measure. The measure is estimated by statistical sampling techniques. It is found empirically that this divergence measure is approximately distributed as a lognormal distribution under various different deformations. Thus a coefficient of variation based on log-divergence provides a natural criterion to compare different types of deformations. It is found that the thin-plate splines deformation is almost optimal over the wider class of the radial type deformations.