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Results 11 - 20
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1,345
Estimation of the drift of fractional Brownian motion
, 2009
"... We consider the problem of efficient estimation for the drift of fractional Brownian motion BH: = ( BH) 1 t with hurst parameter H less than t∈[0,T] 2. We also construct superefficient James-Stein type estimators which dominate, under ..."
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We consider the problem of efficient estimation for the drift of fractional Brownian motion BH: = ( BH) 1 t with hurst parameter H less than t∈[0,T] 2. We also construct superefficient James-Stein type estimators which dominate, under
Drift-free Tracking of Rigid and Articulated Objects
"... Model-based 3D tracker estimate the position, rotation, and joint angles of a given model from video data of one or multiple cameras. They often rely on image features that are tracked over time but the accumulation of small errors results in a drift away from the target object. In this work, we add ..."
Abstract
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Cited by 21 (10 self)
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Model-based 3D tracker estimate the position, rotation, and joint angles of a given model from video data of one or multiple cameras. They often rely on image features that are tracked over time but the accumulation of small errors results in a drift away from the target object. In this work, we
Reducing Drift in Parametric Motion Tracking
, 2001
"... We develop a class of differential motion trackers that automatically stabilize when in finite domains. Most differential trackers compute motion only relative to one previous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a probabilistic ..."
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Cited by 25 (7 self)
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We develop a class of differential motion trackers that automatically stabilize when in finite domains. Most differential trackers compute motion only relative to one previous frame, accumulating errors indefinitely. We estimate pose changes between a set of past frames, and develop a probabilistic
Article Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors
, 2011
"... sensors ..."
Divergence-Free Motion Estimation
- in "ECCV 2012 - European Conference on Computer Vision
, 2012
"... Abstract. This paper describes an innovative approach to estimate mo-tion from image observations of divergence-free flows. Unlike most state-of-the-art methods, which only minimize the divergence of the motion field, our approach utilizes the vorticity-velocity formalism in order to construct a mot ..."
Abstract
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Cited by 3 (1 self)
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Abstract. This paper describes an innovative approach to estimate mo-tion from image observations of divergence-free flows. Unlike most state-of-the-art methods, which only minimize the divergence of the motion field, our approach utilizes the vorticity-velocity formalism in order to construct a
Two-sided estimates on the density of Brownian motion with singular drift
- Ill. J. Math
, 2006
"... Abstract. Let µ = (µ 1,..., µ d) be such that each µ i is a signed measure on R d belonging to the Kato class Kd,1. The existence and uniqueness of a continuous Markov process X on R d, called a Brownian motion with drift µ, was recently established by Bass and Chen. In this paper we study the poten ..."
Abstract
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Cited by 31 (23 self)
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Abstract. Let µ = (µ 1,..., µ d) be such that each µ i is a signed measure on R d belonging to the Kato class Kd,1. The existence and uniqueness of a continuous Markov process X on R d, called a Brownian motion with drift µ, was recently established by Bass and Chen. In this paper we study
Estimating Displacement of Periodic Motion With Inertial Sensors
"... Abstract—Inertial sensors, like accelerometers and gyroscopes, are rarely used by themselves to measure displacement. Accuracy of inertial sensors is greatly handicapped by the notorious integration drift, which arises due to numerical integration of the sensors zero bias error. A solution is propos ..."
Abstract
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Cited by 3 (2 self)
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is proposed in this paper to provide drift free estimation of displacement from inertial sensors. Index Terms—Inertial sensors, integration drift, periodic motion estimation. I.
Structure from motion causally integrated over time
- IEEE TRANS PATTERN ANALYSIS & MACHINE INTELLIGENCE
, 2002
"... We describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the estimation error remains bounded with probability one throughout a sequence of arbitr ..."
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Cited by 109 (4 self)
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We describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the estimation error remains bounded with probability one throughout a sequence
Results 11 - 20
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1,345
