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A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

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Abstract

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach

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Authors and Affiliations

  1. PAMI Research Group, E & CE, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    H. Zhu

  2. Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    O. Basir

Authors
  1. H. Zhu
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  2. O. Basir
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Correspondence to H. Zhu.

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Zhu, H., Basir, O. A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing. Soft Comput 10, 1169–1180 (2006). https://doi.org/10.1007/s00500-005-0039-1

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  • DOI: https://doi.org/10.1007/s00500-005-0039-1

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