Deformable image registration is a fundamental task in medical image processing. Among
its most important applications, one may cite: 1) multi-modality fusion, where information …

Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large
spatial transformations in image data. The model has, however, seen limited uptake as most …

We propose a new diffeomorphic matching algorithm and use it to learn nonlinear dynamical
systems with the guarantee that the learned systems have global asymptotic stability. For a …

We propose novel finite-dimensional spaces of well-behaved transformations. The latter are
obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity …

Object morphology, defined as shape and size characteristics, observed on medical imagery
is often an important marker for disease presence and/or aggressiveness. In the context of …

We propose novel finite-dimensional spaces of RR transformations, n [?] 1, 2, 3, derived
from (continuously-defined) parametric stationary velocity fields. Particularly, we obtain …

We propose a new method to obtain landmark-matching transformations between n-
dimensional Euclidean spaces with large deformations. Given a set of feature …

Shape matching plays a prominent role in the comparison of similar structures. We present a
unifying framework for shape matching that uses mixture models to couple both the shape …

This work presents a deformable point set registration algorithm that seeks an optimal set of
radial basis functions to describe the registration. A novel, global optimization approach is …

We introduce a labeled point set registration algorithm based on a family of novel
information-theoretic measures derived as a generalization of the well-known Shannon …