Robust fusion of uncertain information

Authors:
Haifeng Chen;P. Meer
Affiliations:
NEC Labs. America Inc., Princeton, NJ, USA;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2005

Citing 0
Cited 7

Mean shift-based clustering

Pattern Recognition
A flexible multi-source spatial-data fusion system for environmental status assessment at continental scale

International Journal of Geographical Information Science
Synthetic data generation technique in Signer-independent sign language recognition

Pattern Recognition Letters
Nonlinear Mean Shift over Riemannian Manifolds

International Journal of Computer Vision
Multi-sensor IMM estimator for uncertain measurement

WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A driver fatigue recognition model based on information fusion and dynamic Bayesian network

Information Sciences: an International Journal
On the convergence of the mean shift algorithm in the one-dimensional space

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

A technique is presented to combine n data points, each available with point-dependent uncertainty, when only a subset of these points come from N≪n sources, where N is unknown. We detect the significant modes of the underlying multivariate probability distribution using a generalization of the nonparametric mean shift procedure. The number of detected modes automatically defines N, while the belonging of a point to the basin of attraction of a mode provides the fusion rule. The robust data fusion algorithm was successfully applied to two computer vision problems: estimating the multiple affine transformations, and range image segmentation.