16 December 1992 Inference for data fusion
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Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992); doi: 10.1117/12.130872
Event: San Diego '92, 1992, San Diego, CA, United States
Abstract
Data fusion has been widely used in various fields of automation. This paper describes a multisensor integration system: range and intensity image processing system, which can be used for object recognition and classification. In the data fusion processing, a new method called generalized evidence inference method is used by the system. The method presented here unifies both Bayesian theory and Dempster-Shafer's evidential reasoning (DSER) for the combination of information from diversified sources, and overcomes the disadvantages of both approaches. At the same time, we adopt these three approaches: the Bayesian theory, the DSER, and the unified approach to fuse the reports in the system for object recognition and classification, the results are compared and analyzed.
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Lei-Jian Liu, Y. G. Gu, Jingyu Yang, "Inference for data fusion", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130872; https://doi.org/10.1117/12.130872
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KEYWORDS
Image processing

Sensors

Data fusion

3D image processing

3D modeling

Probability theory

Image fusion

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