Robust regression and outlier detection
Robust regression and outlier detection
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust computer vision: a least median of squares based approach
Proceedings of a workshop on Image understanding workshop
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
On Navigating Between Friends and Foes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Robust Line Fitting in a Noisy Image by the Method of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comments on: 'Robust Line Fitting in a Noisy Image by the Method of Moments'
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A highly robust estimator for regression models
Pattern Recognition Letters
Statistical properties of the Hough transform estimator in the presence of measurement errors
Journal of Multivariate Analysis
Contour Detection for Industrial Image Processing by Means of Level Set Methods
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Corner detection and curve segmentation by multiresolution chain-code linking
Pattern Recognition
Renewal strings for cleaning astronomical databases
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
| Hi-index | 0.14 |
The problem of fitting a straight line to a planar set of points is reconsidered. A parameter space computational approach capable of fitting one or more lines to a set of points is presented. The suggested algorithm handles errors in both coordinates of the data points, even when the error variances vary between coordinates and among points and can be readily made robust to outliers. The algorithm is quite general and allows line fitting according to several useful optimality criteria to be performed within a single computational framework. It is observed that certain extensions of the Hough transform can be turned to be equivalent to well-known M estimators, thus allowing computationally efficient approximate M estimation.