Fusion of Multisensor Data in Pattern Recognition

Abstract

This paper describes a number of techniques by which to fuse multisensor data (images, signals, scenes, etc..) and by which to generate higher level representations of an unknown pattern within the context of pattern recognition. The basic steps involved are:

• A. locate a representation based on exogenous context information

• B. compare two representations to find out if they refer to the same entity

• C. merging features from two representations of the same pattern into a new feature

• D. aggregaring two representations into a higher level representation.

The importance of information fusion in pattern recognition stems first from the fact that it is generally correct to assume that improvements in terms of classification error can only be achieved at the expense of additional independent features provided by more separate sensors.