Abstract We consider instances of the Stable Roommates problem that arise from geometric representation of participants' preferences: a participant is a point in a metric space, and his preference list is given by the sorted list of distances to the other participants. We show that contrary

In this paper we study the potential theory of symmetric geometric stable processes by realizing them as subordinate Brownian motions with geometric stable subordinators. More precisely, we establish the asymptotic behaviors of the Green function and the Lévy density of symmetric geometric stable

this paper should provide a more useful and realistic class of distributions for portfolio modeling. The more general case where the number of summands has an arbitrary distribution is discussed in a companion paper [18]. The focus of this paper on geometric summation allows a simpler treatment

The Iterative Closest Point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.

Abstract. Let (Xi) be a sequence of i.i.d. random variables, and let N be a geometric random variable independent of (Xi). Geometric stable distributions are weak limits of (normalized) geometric compounds, SN = X1 + · · · + XN, when the mean of N converges to infinity. By an appro

Abstract. The aim of this article is to study geometric F-semistable and geometric F-stable distributions on the d-dimensional lattice Zd+. We obtain several properties for these distributions, including characterizations in terms of their probability generating functions. We describe a relation be

. The explicit form of L'evy measure for geometric stable (GS) random variables follows from the general L'evy--Kchintchine representation of a subordinated infinitely divisible process. Through this form, asymptotic properties of L'evy measure are studied. In particular, logarithmic

segmentation allows to connect classical “snakes ” based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps

in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients

This paper consists of two parts, both of which address stability of perfect matchings. In the first part we consider instances of the Stable Roommates problem that arise from geometric representation of participants preferences: a participant is a point in Euclidean space, and his preference list