Robust regression and outlier detection
Robust regression and outlier detection
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
MUSE: Robust Surface Fitting using Unbiased Scale Estimates
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond RANSAC: User Independent Robust Regression
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
| Hi-index | 0.00 |
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 diagnostic method that uses random sampling like RANSAC, but adaptively estimates the inlier scale using a novel adaptive scale estimator The residual distribution model of inliers is assumed known, such as a Gaussian distribution Given a putative solution, our inlier scale estimator attempts to extract a distribution for the inliers from the distribution of all residuals This is done by globally searching a partition of the total distribution that best fits the Gaussian distribution Then, the density of the residuals of estimated inliers is used as the score in the objective function to evaluate the putative solution The output of the estimator is the best solution that gives the highest score Experiments with various simulations and real data for line fitting and fundamental matrix estimation are carried out to validate our algorithm, which performs better than several of the latest robust estimators.