Data fusion through nondeterministic approaches: a comparison

Muhamad Abdulghafour; Mongi A. Abidi

doi:10.1117/12.150253

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Abstract

Information gathered by different knowledge sources from the same scene are often uncertain, imprecise, fuzzy, or incomplete. Using a multi-sensory system to integrate several types of data should yield more meaningful information otherwise unavailable or difficult to acquire by a single sensory modality. In this paper, we examine a number of non-deterministic methods for solving the fusion problem. Within the framework of fuzzy sets theory, we present a new technique for data fusion. We develop a fusion formula based on the measure of fuzziness. The fusion formula is mathematically tested against several desirable properties of fusion operators. Also, the Super Bayesian Approach and Dempster's rule of combination are presented. These approaches were implemented and tested with real range and intensity images acquired by an Odetics Laser Range Scanner. The goal was to obtain better scene descriptions through a segmentation process of both images. A systematic method for evaluating and comparing segmentation results was presented. Various levels of noise were added to the real data and segmentation results from all three approaches were evaluated.

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Muhamad Abdulghafour and Mongi A. Abidi "Data fusion through nondeterministic approaches: a comparison", Proc. SPIE 2059, Sensor Fusion VI, (20 August 1993); https://doi.org/10.1117/12.150253

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