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PAMI
2012
8 years 6 months ago
Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers
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...
Hanzi Wang, Tat-Jun Chin, David Suter
ICRA
2009
IEEE
210views Robotics» more  ICRA 2009»
10 years 1 months ago
An adaptive-scale robust estimator for motion estimation
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...
Trung Ngo Thanh, Hajime Nagahara, Ryusuke Sagawa, ...
PAMI
2010
146views more  PAMI 2010»
10 years 2 months ago
A Generalized Kernel Consensus-Based Robust Estimator
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...
Hanzi Wang, Daniel Mirota, Gregory D. Hager
SMA
2008
ACM
162views Solid Modeling» more  SMA 2008»
10 years 4 months ago
Fast and robust bootstrap
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...
Matias Salibian-Barrera, Stefan Van Aelst, Gert Wi...
CSDA
2010
119views more  CSDA 2010»
10 years 4 months ago
Fast robust estimation of prediction error based on resampling
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...
Jafar A. Khan, Stefan Van Aelst, Ruben H. Zamar
ADAC
2008
193views more  ADAC 2008»
10 years 4 months ago
A constrained-optimization based half-quadratic algorithm for robustly fitting sets of linearly parametrized curves
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...
Jean-Philippe Tarel, Sio-Song Ieng, Pierre Charbon...
ACCV
2009
Springer
10 years 5 months ago
Adaptive-Scale Robust Estimator Using Distribution Model Fitting
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...
Trung Ngo Thanh, Hajime Nagahara, Ryusuke Sagawa, ...
SODA
1997
ACM
171views Algorithms» more  SODA 1997»
10 years 5 months ago
A Practical Approximation Algorithm for the LMS Line Estimator
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...
David M. Mount, Nathan S. Netanyahu, Kathleen Roma...
ICASSP
2008
IEEE
10 years 10 months ago
Maximum kernel density estimator for robust fitting
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...
Hanzi Wang
KDD
2002
ACM
113views Data Mining» more  KDD 2002»
11 years 4 months ago
Scalable robust covariance and correlation estimates for data mining
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...
Fatemah A. Alqallaf, Kjell P. Konis, R. Douglas Ma...
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