Results 1  10
of
12,067
is the standard Brownian motion with
"... ns of (1) with the same # # [1, 1] \ {0}, relative to the same Brownian motion B t , then X x t = X y t for some t < #, a.s. Proof. For simplicity assume that # > 0 and 0 = x < y. Let b L 0 t = #L 0 t , b L y t = y + #L y t , 1. Research partially supported by an NSERC (C ..."
Abstract
 Add to MetaCart
ns of (1) with the same # # [1, 1] \ {0}, relative to the same Brownian motion B t , then X x t = X y t for some t < #, a.s. Proof. For simplicity assume that # > 0 and 0 = x < y. Let b L 0 t = #L 0 t , b L y t = y + #L y t , 1. Research partially supported by an NSERC
Stochastic Differential Equations Driven by Fractional Brownian Motion and Standard Brownian Motion
, 2008
"... We prove an existence and uniqueness theorem for solutions of multidimensional, time dependent, stochastic differential equations driven simultaneously by a multidimensional fractional Brownian motion with Hurst parameter H> 1/2 and a multidimensional standard Brownian motion. The proof relies on ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
We prove an existence and uniqueness theorem for solutions of multidimensional, time dependent, stochastic differential equations driven simultaneously by a multidimensional fractional Brownian motion with Hurst parameter H> 1/2 and a multidimensional standard Brownian motion. The proof relies
4. Nondifferentiability of Standard Brownian Motion 8
"... Abstract. We give an overview of standard onedimensional Brownian motion on the dyadic rationals. We then offer a proof that such a process is uniformly continuous on closed intervals and, hence, that the original definition can be sensibly extended to all nonnegative reals. We conclude by investig ..."
Abstract
 Add to MetaCart
Abstract. We give an overview of standard onedimensional Brownian motion on the dyadic rationals. We then offer a proof that such a process is uniformly continuous on closed intervals and, hence, that the original definition can be sensibly extended to all nonnegative reals. We conclude
where B is a kvariate standard Brownian motion on the unit interval,
, 2010
"... Phillips (1988) has set forth conditions on a kvariate time series process xt such that, with St = Pt 1 PT j=1 xj, T t=1 St−1x0 t converges in distribution to the stochastic matrix Σ1/2 R ..."
Abstract
 Add to MetaCart
Phillips (1988) has set forth conditions on a kvariate time series process xt such that, with St = Pt 1 PT j=1 xj, T t=1 St−1x0 t converges in distribution to the stochastic matrix Σ1/2 R
On probability characteristics of downfalls in a standard brownian motion. Teoriya Veroyatnostei i ee Primeneniya
, 1999
"... Abstract. For a Brownian motion B = (Bt)t1 with B0 = 0, EBt = 0, EB ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
Abstract. For a Brownian motion B = (Bt)t1 with B0 = 0, EBt = 0, EB
Variation of iterated Brownian motion
 In MeasureValued Processes, Stochastic Partial Differential Equations and Interacting Systems
, 1994
"... standard Brownian motions starting from 0 and let (1) X(t) = X1(t) if t ≥ 0, ..."
Abstract

Cited by 32 (4 self)
 Add to MetaCart
standard Brownian motions starting from 0 and let (1) X(t) = X1(t) if t ≥ 0,
Representing Moving Images with Layers
, 1994
"... We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each o ..."
Abstract

Cited by 542 (11 self)
 Add to MetaCart
other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image
Randomized kinodynamic planning
 THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2001; 20; 378
, 2001
"... This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based ..."
Abstract

Cited by 626 (35 self)
 Add to MetaCart
dynamical models and avoiding obstacles in the robot’s environment. The authors consider generic systems that express the nonlinear dynamics of a robot in terms of the robot’s highdimensional configuration space. Kinodynamic planning is treated as a motionplanning problem in a higher dimensional state
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a ViewBased Representation
 International Journal of Computer Vision
, 1998
"... This paper describes an approach for tracking rigid and articulated objects using a viewbased representation. The approach builds on and extends work on eigenspace representations, robust estimation techniques, and parameterized optical flow estimation. First, we note that the leastsquares image r ..."
Abstract

Cited by 656 (16 self)
 Add to MetaCart
reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation. Second we define a "subspace constancy assumption" that allows us to exploit techniques for parameterized optical flow estimation to solve for both
MonoSLAM: Realtime single camera SLAM
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—We present a realtime algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the “pure vision ” domain of ..."
Abstract

Cited by 490 (26 self)
 Add to MetaCart
an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC
Results 1  10
of
12,067