|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
IMAGE REGISTRATION USING MULTI-SCALE TEXTURE MOMENTS
Jun Sato and Roberto Cipolla
In this paper we propose a novel, efficient and geometrically intuitive method to compute the four components of an affine transformation from the change in simple statistics of images of texture. In particular, we show how the changes in second circular moments of edge orientation are directly related to the rotation (curl), scale (divergence) and deformation components of an affine transformation, and how these components can be computed from multi-scale texture moments. A simple implementation is described which does not require point, edge or contour correspondences to be established. It is tested on repetitive and non-repetitive visual textures which are neither isotropic nor homogeneous. The theoretical accuracy and the noise sensitivity of this method are compared with other linear moment and circular moment methods.
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