Three dimensional transparent structure segmentation and multiple 3D motion estimation from monocular perspective image sequences

A three dimensional scene can be segmented using different cues, such as boundaries, texture, motion, discontinuities of the optical flow, stereo, models for structure, etc. We investigate segmentation based upon one of these cues, namely three dimensional motion. If the scene contain transparent objects, the two dimensional (local) cues are inconsistent, since neighboring points with similar optical flow can correspond to different objects. We present a method for performing three dimensional motion-based segmentation of (possibly) transparent scenes together with recursive estimation of the motion of each independent rigid object from monocular perspective images. Our algorithm is based on a recently proposed method for rigid motion reconstruction and a validation test which allows us to initialize the scheme and detect outliers during the motion estimation procedure. The scheme is tested on challenging real and synthetic image sequences. Segmentation is performed for the Ullmann's experiment of two transparent cylinders rotating about the same axis in opposite directions

Three-dimensional effects on pure tone fan noise due to inflow distortion

Two dimensional, quasi three dimensional and three dimensional theories for the prediction of pure tone fan noise due to the interaction of inflow distortion with a subsonic annular blade row were studied with the aid of an unsteady three dimensional lifting surface theory. The effects of compact and noncompact source distributions on pure tone fan noise in an annular cascade were investigated. Numerical results show that the strip theory and quasi three-dimensional theory are reasonably adequate for fan noise prediction. The quasi three-dimensional method is more accurate for acoustic power and model structure prediction with an acoustic power estimation error of about plus or minus 2db

The estimation of three-dimensional fixed effects panel data models

The paper introduces for the most frequently used three-dimensional fixed effects panel data models the appropriate Within estimators. It analyzes the behaviour of these estimators in the case of no-self-flow data, unbalanced data and dynamic autoregressive models.panel data, unbalanced panel, dynamic panel data model, multidimensional panel data, fixed effects, trade models, gravity models, FDI

COINTEGRATION VECTOR ESTIMATION BY DOLS FOR A THREE-DIMENSIONAL PANEL

This paper extends the asymptotic results of the dynamic ordinary least squares (DOLS) cointegration vector estimator of Mark and Sul (2003) to a three-dimensional panel. We use a balanced panel of N and M lengths observed over T time periods. The cointegration vector is homogenous across individuals but we allow for individual heterogeneity using different short-run dynamics, individual-specific fixed effects and individual-specific time trends. Both individual effects are considered for the first two dimensions. This paper was motivated by the three-dimensional panel cointegration analysis used to estimate the total factor productivity for Colombian regions and sectors during 1975-2000 by Iregui, Melo and Ramírez (2007). They used the methodology proposed by Marrocu, Paci and Pala (2000); however, hypothesis testing is not valid under this technique. The methodology we are currently proposing allows us to estimate the long-run relationship and to construct asymptotically valid test statistics in the 3D-panel context.Cointegration, Dynamic OLS estimation, panel data in three dimensions. Classification JEL: C13; C33.

Triggered qutrits for Quantum Communication protocols

A general protocol in Quantum Information and Communication relies in the ability of producing, transmitting and reconstructing, in general, qunits. In this letter we show for the first time the experimental implementation of these three basic steps on a pure state in a three dimensional space, by means of the orbital angular momentum of the photons. The reconstruction of the qutrit is performed with tomographic techniques and a Maximum-Likelihood estimation method. In this way we also demonstrate that we can perform any transformation in the three dimensional space

Characterization of the asymptotic distribution of semiparametric M-estimators

This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator. © 2010 Elsevier B.V. All rights reserved

Estimation for bilinear stochastic systems

Three techniques for the solution of bilinear estimation problems are presented. First, finite dimensional optimal nonlinear estimators are presented for certain bilinear systems evolving on solvable and nilpotent lie groups. Then the use of harmonic analysis for estimation problems evolving on spheres and other compact manifolds is investigated. Finally, an approximate estimation technique utilizing cumulants is discussed