To define a dissimilarity measure between geometrical structures, we propose to use a representation of shapes with normal cycles (an object associated with the normal bundle of the shape which encodes all the curvature information). Using kernel metrics on normal cycles, we define a metric between shapes that fits in a framework of inexact registration. It allows for a matching that takes into account the region of high curvature and the boundaries of the shapes.

This presentation is part of Minisymposium “MS75 - Geometric methods for shape analysis with applications to biomedical imaging and computational anatomy, Part II (2 parts)”
organized by: Joan Alexis Glaunès (MAP5, Université Paris Descartes) , Sergey Kushnarev (Singapore University of Technology and Design) , Mario Micheli (Harvey Mudd College) .

Authors:

Joan Alexis Glaunès (MAP5, Université Paris Descartes)

Pierre Roussillon (Centre de Mathématiques et de Leurs Applications, Ecole Normale Supérieure Paris-Saclay)

Keywords:

geometric measure theory, image registration, kernel methods, shape analysis