Abstract
In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of context for observed features conferring to the solution a great flexibility for managing different type of features with wrong and missing values as required by many real applications. Moreover, modifying opportunely the messages that flow into the network, we obtain an effective way to condition the inference based on the different reliability of each information source or in presence of single unreliable signal. The proposed architecture has been used to fuse different detectors for an identity document classification task but its flexibility, extendibility and robustness make it suitable to many real scenarios where the signal can be wrongly received or completely missing.
Highlights
Data Fusion techniques are becoming increasingly important in many application contexts, such as defence, energy, biomedicine, manufacturing, etc
We described an Information Fusion architecture using the Factor Graph in Reduced Normal Form paradigm
We demonstrated how it is possible to condition the system in the presence of information sources with different reliability or in presence of single unreliable detection
Summary
Data Fusion techniques are becoming increasingly important in many application contexts, such as defence, energy, biomedicine, manufacturing, etc. Fusion methods lead to better understanding of a phenomenon and of the decisions to be taken, especially in terms of robustness and accuracy with respect to what we would obtain using separate sources of information [1]. We can identify three increasing abstraction levels of Data Fusion models: Data Level, Feature Level and Decision Level. Dasarathy [2] has proposed five fusion modes : Data. In–Data Out (DaI-DaO) Fusion, Data In–Feature Out (DaI-FeO) Fusion, Feature In–Feature. Out (FeI-FeO) Fusion, Feature In–Decision Out (FeI-DeO) Fusion, Decision In–Decision. We investigate the application of a Bayesian approach to the FeI-DeO. The input features, coming from different sensors, are merged to produce a more informed decision
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