Dynamic Confinement, Classification, and Imaging

Abstract

The problem of matching two images of the same objects but after movements or slight deformations arises in medical imaging, but also in the microscopic analysis of physical or biological structures. We present a new matching strategy consisting of two steps. We consider the grey level function (modulo a normalization) as a probability density function. First, we apply a density based clustering method in order to obtain a tree or more generally a hierarchy which classifies the points on which the grey level function is defined. Secondly, we use the identification of the hierarchical representation of the two images to guide the image matching or to define a distance between the images for object recognition. The transformation invariance properties of the representations, that we will demonstrate, permit to extract invariant image points. But in addition, using the identification of the hierarchical structures, they permit also to find the correspondence between invariant points even if these have moved locally. Finally, we mention possibilities to construct hierarchies which integrate more geometrical information. The method’s results on real images will be discussed.