Robust fusion of uncertain information

Haifeng Chen Peter Meer

Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA

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 appurtenance of a point to the basin of attraction of a mode provides the fusion rule.

IEEE Workshop on Learning in Computer Vision and Pattern Recognition, Madison, WI, June 2003.
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