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MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
 Computer Vision and Image Understanding
, 2000
"... A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solu ..."
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Cited by 359 (10 self)
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A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative
Robustness Estimates for
"... of regulator theory on differentiable manifolds, ” presented at the ..."
Robust Estimators are Hard to Compute
, 2006
"... In modern statistics, the robust estimation of parameters of a regression hyperplane is a central problem. Robustness means that the estimation is not or only slightly affected by outliers in the data. In this paper, it is shown that the following robust estimators are hard ..."
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Cited by 7 (0 self)
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In modern statistics, the robust estimation of parameters of a regression hyperplane is a central problem. Robustness means that the estimation is not or only slightly affected by outliers in the data. In this paper, it is shown that the following robust estimators are hard
Robust estimation in geodetic networks
"... The application of robust estimation to geodetic networks is analysed versus the classical leastsquares approach. In case of gross or systematic errors appearance either in the mathematical model or in the observations to be adjusted, leastsquares estimation along with detection statistical tests ..."
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The application of robust estimation to geodetic networks is analysed versus the classical leastsquares approach. In case of gross or systematic errors appearance either in the mathematical model or in the observations to be adjusted, leastsquares estimation along with detection statistical tests
ON THE ROBUST ESTIMATION OF ECONOMETRIC MODELS
"... Most of the work that has been done on robust estimation techniques has been concerned with the estimation of a small number of parameters. ’ This paper considers the use of such techniques for the estimation of econometric models. The computational aspects of obtaining robust estimates of a general ..."
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Most of the work that has been done on robust estimation techniques has been concerned with the estimation of a small number of parameters. ’ This paper considers the use of such techniques for the estimation of econometric models. The computational aspects of obtaining robust estimates of a
Portfolio Selection with Robust Estimation
 SUBMITTED TO OPERATIONS RESEARCH
, 2007
"... Meanvariance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly outofsample due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason, pra ..."
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Cited by 29 (1 self)
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that fluctuate substantially over time. In this paper, we propose a class of portfolios that have better stability properties than the traditional minimumvariance portfolios. The proposed portfolios are constructed using certain robust estimators and can be computed by solving a single nonlinear program, where
Robust Estimates for GARCH Models
"... In this paper we present two robust estimates for GARCH(p,q) models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estim ..."
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Cited by 7 (0 self)
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In this paper we present two robust estimates for GARCH(p,q) models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next
Robust Estimation for Motion Parameters
"... The performance of least squares method ca'n be improved by changing the error metric so that points which lie far from the bulk of data do not influence the final value—that is to reject the outliers. In this paper, the combination of two robust estimators are used to obtain motion parameters ..."
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The performance of least squares method ca'n be improved by changing the error metric so that points which lie far from the bulk of data do not influence the final value—that is to reject the outliers. In this paper, the combination of two robust estimators are used to obtain motion parameters
Image Simplification By Robust Estimator
 Proc. 16th Int. Symposium On Computer and Information Sciences
, 2001
"... In this contribution, we discuss a robust estimator based on image reconstruction technique for image filtering and simplification purposes. Instead of using the leastsquares estimators that the measurement error is independently random and distributed as a normal distribution, a Lorentzian dist ..."
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In this contribution, we discuss a robust estimator based on image reconstruction technique for image filtering and simplification purposes. Instead of using the leastsquares estimators that the measurement error is independently random and distributed as a normal distribution, a Lorentzian
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 ..."
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Cited by 656 (16 self)
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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
Results 1  10
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937,076