In this paper, we investigate two stochastic perturbations of the metamorphosis equations of image analysis, in the geometrical context of the EulerPoincaré theory. In the metamorphosis of images, the Lie group of diffeomorphisms deforms a template image that is undergoing its own internal dynamics as it deforms. This type of deformation allows more freedom for image matching and has analogies ...

Ambient ozone (O(3)) is an important phytotoxic pollutant, and detailed knowledge of its spatial distribution is becoming increasingly important. The aim of the paper is to compare different spatial interpolation techniques and to recommend the best approach for producing a reliable map for O(3) with respect to its phytotoxic potential. For evaluation we used real-time ambient O(3) concentratio...

We consider the inverse sensitivity analysis problem of quantifying the uncertainty of inputs to a deterministic map given specified uncertainty in a linear functional of the output of the map. This is a version of the model calibration or parameter estimation problem for a deterministic map. We assume that the uncertainty in the quantity of interest is represented by a random variable with a g...

In vector model, the boundary between features is stored and shown categorically in an absolute way, in other words Membership or non-membership of a point in each polygon is known which can be 0 or 1. For showing real world and its projection on map, because of the uncertainty on the border of the boundary between phenomena and spatial data, there is some uncertainty and vagueness in drawing a...

A chaotic system owns complex dynamics though it is with deterministic expression in mathematics, and it has attractived numerous study and application in many fields of the world. But for a concrete application, it is usually with parameters uncertainty while it is implemented. In this paper synchronization of discrete-time chaotic systems with parameters and/or structure uncertainty is resear...

Representing uncertainty in predictions made by complex probabilistic models is often crucial but computationally challenging. There are a number of useful probabilistic models where computing the most probably assignment (or MAP) is easy but finding the marginal probability of variables is hard. In this paper, we present a novel representation of uncertainty in discrete probabilistic models, t...

This paper describes an adaptive robot speed control method for safe and efficient navigation in unknown environments. Safety and efficiency are usually in a trade-off relationship. Moving fast increases efficiency but decreases safety due to low reliability in localization and environment recognition; moving slowly decreases efficiency but increases safety. Speed control considering this trade...

The subject of this thesis is belief function theory and its application in different contexts. Belief function theory (also known as Dempster-Shafer theory) is a mathematical framework for describing quantified beliefs held by an agent. It can be interpreted as a generalization of Bayesian probability theory and makes it possible to distinguish between different types of uncertainty. In partic...

In this paper we propose a methodology to generate trajectories for a mobile robot which uses an uncertain geometric map to estimate its position and orientation during its displacement. This methodology is based on the definition of particular paths and on defining a cost function to weigh the trade-off between expected localization gain and the risk due to the map uncertainty.

This paper describes an adaptive robot speed control method for safe and efficient navigation in unknown environments. Speed control is important in the following two cases. (1) When a robot enters a narrow free space, it needs to control the speed to avoid any collision considering the motion uncertainty. (2) When a robot enters a region whose vacancy (i.e., being free) has not been decided ye...