In this paper we propose a novel approach for incorporating measures of spatial uncertainty which are derived from non-rigid registration, into propagated segmentation labels. In current approaches to segmentation via label propagation, a point-estimate of the registration parameters is used. However, this is limited by the registration accuracy achieved. In this work, we derive local measureme...

Weak probabilistic bisimulation on probabilistic automata can be decided by an algorithm that needs to check a polynomial number of linear programming problems encoding weak transitions. It is hence polynomial, but not guaranteed to be strongly polynomial. In this paper we show that for polynomial rational probabilistic automata strong polynomial complexity can be ensured. We further discuss co...

Weak distribution bisimilarity is an equivalence notion on probabilistic automata, originally proposed for Markov automata. It has gained some popularity as the coarsest behavioral equivalence enjoying valuable properties like preservation of trace distribution equivalence and compositionality. This holds in the classical context of arbitrary schedulers, but it has been argued that this class o...

Living organisms survive and multiply even though they have uncertain and incomplete information about their environment and imperfect models to predict the consequences of their actions. Bayesian models have been proposed to face this challenge. Indeed, Bayesian inference is a way to do optimal reasoning when only uncertain and incomplete information is available. Various perceptive, sensory-m...

This paper presents a technique, Probabilistic Road Map (PRM) planning and its application to aircraft for rapidly generating routes using aggressive maneuvers to avoid incoming munitions, terrain, and other aircraft while trying to maintain other mission objectives. The technique was applied to a high fidelity simulation and shown to always generate correct routes within its 0.1 seconds deadli...

Rationing occurs if the demand for a certain good exceeds its supply. In such situations a rationing method has to be specified in order to determine the allocation of the scarce good to the agents. Moulin (1999) introduced the notion of probabilistic rationing methods for the discrete framework. In this paper we establish a link between classical and probabilistic rationing methods. In particu...

Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...