Research on parameterized algorithmics for NP-hard problems has steadily grown over the
last years. We survey and discuss how parameterized complexity analysis naturally …

Given n axis-parallel boxes in a fixed dimension d≥ 3, how efficiently can we compute the
volume of the union? This standard problem in computational geometry, commonly referred …

We are studying d-dimensional geometric problems that have algorithms with 1− 1/d
appearing in the exponent of the running time, for example, in the form of 2n1− 1/d or nk1 …

We present a (combinatorial) algorithm with running time close to O (n^ d) for computing the
minimum directed L_∞ Hausdorff distance between two sets of n points under translations …

In this paper, we consider the problem (denoted as EMDRT) of minimizing the earth mover's
distance between two sets of weighted points A and B in R^ d R d under rigid transformation …

We present an embedding and search reduction which allow us to build the first data
structure for the nearest neighbor search among small point sets with respect to the directed …

We present a (combinatorial) algorithm with running time close to $ O (n^ d) $ for computing
the minimum directed $ L_\infty $ Hausdorff distance between two sets of $ n $ points under …

In this paper, we consider the problem (denoted as EMDRT) of minimizing the earth mover's
distance between two sets of weighted points A and B in a fixed dimensional ℝ d space …

Given a set A={a1,…, an} of n image points and a set B={b1,…, bn} of n model points, the
problem is to find a transformation matching (a one-to-one mapping) each point a∈ A to …

While Deep-Learning approaches beat Nearest-Neighbor classifiers in an increasing
number of areas, searching existing uncertain data remains an exclusive task for similarity …