PAMI
2012
12 years 2 months ago
2012
Abstract—We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiplestructure data even in the presence of a large number...
ICRA
2009
IEEE
13 years 10 months ago
2009
IEEE
Although RANSAC is the most widely used robust estimator in computer vision, it has certain limitations making it ineffective in some situations, such as the motion estimation prob...
PAMI
2010
13 years 11 months ago
2010
In this paper, we present a new Adaptive Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANSAC (R...
SMA
2008
ACM
14 years 10 days ago
2008
ACM
The Least Trimmed Squares (LTS) estimator is a frequently used robust estimator of regression. When it comes to inference for the parameters of the regression model, the asymptoti...
CSDA
2010
14 years 16 days ago
2010
Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of t...
ADAC
2008
14 years 17 days ago
2008
We consider the problem of multiple fitting of linearly parametrized curves, that arises in many computer vision problems such as road scene analysis. Data extracted from images us...
ACCV
2009
Springer
14 years 1 months ago
2009
Springer
We propose a new robust estimator for parameter estimation in highly noisy data with multiple structures and without prior information on the noise scale of inliers. This is a diag...
SODA
1997
ACM
14 years 1 months ago
1997
ACM
The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because...
ICASSP
2008
IEEE
14 years 6 months ago
2008
IEEE
Robust model fitting plays an important role in many computer vision applications. In this paper, we propose a new robust estimator — Maximum Kernel Density Estimator (MKDE) bas...
KDD
2002
ACM
15 years 25 days ago
2002
ACM
Covariance and correlation estimates have important applications in data mining. In the presence of outliers, classical estimates of covariance and correlation matrices are not re...