Extracting geometric primitives
CVGIP: Image Understanding
Bias in Robust Estimation Caused by Discontinuities and Multiple Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Computer Vision through Kernel Density Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Clusters, outliers, and regression: fixed point clusters
Journal of Multivariate Analysis
Detection of linear and circular shapes in image analysis
Computational Statistics & Data Analysis
Robust fitting of mixture regression models
Computational Statistics & Data Analysis
Robust mixture regression using the t-distribution
Computational Statistics & Data Analysis
Hi-index | 0.00 |
We use the local maxima of a redescending M-estimator to identify cluster, a method proposed already by Morgenthaler (in: H.D. Lawrence, S. Arthur (Eds.), Robust Regression, Dekker, New York, 1990, pp. 105-128) for finding regression clusters. We work out the method not only for classical regression but also for orthogonal regression and multivariate location and show that all three approaches are special cases of a general approach which includes also other cluster problems. For the general case we show consistency for an asymptotic objective function which generalizes the density in the multivariate case. The approach of orthogonal regression is applied to the identification of edges in noisy images.